{"title":"Cloud Ninjas Workstations for AI \u0026 GPU-Heavy Softwares","description":" \u003cp class=\"hero-p\"\u003eCloud Ninjas new workstations meant for AI and heavy GPU softwares are the perfect solution for your business.\n Cloud Ninjas's offers a variety of selections to cater to your business infrastructure. Ranging from ITX to ATX cases, consumer or enterprise AMD or Intel CPUs, and the latest and most performant NVIDIA GPUs. Cloud Ninjas brings what you need to run any AI model you need, any machine learning workload, 3D-model, video editing capabilities. Built with the best components in the market, your infrastructure will be up to today's standards. Check our collection and build your own workstation.\n \u003c\/p\u003e","products":[{"product_id":"cloud-ninjas-watru-4n6s-4g-ai-workstation","title":"Mythical Dragon AI Workstation","description":"\u003cdiv class=\"subsection cpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"cpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eProcessor Specifications for Cloud Ninjas Mythical Dragon\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eCloud Ninjas Mythical Dragon focus in offering the best AMD Ryzen Threadripper processors, to allow the maximum quantity of PCIe lane availability to power AI workstations with multi-GPU capabilities and the best storage performance allowing not only SATA but also NVMe U.2 solid state drives. Check below list of compatible AMD Ryzen threadripper CPUs, compare and choose the best choice for your infrastructure. \u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n \u003cdiv class=\"table-section\"\u003e\n \u003cdiv class=\"cpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eCPU\u003c\/th\u003e\n \u003cth\u003eCores \u0026amp; Threads\u003c\/th\u003e\n \u003cth\u003eBase Clock\u003c\/th\u003e\n \u003cth\u003eTurbo Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen Threadripper PRO 7965WX\u003c\/td\u003e\n \u003ctd\u003e24C\/48T\u003c\/td\u003e\n \u003ctd\u003e4.20 GHz\u003c\/td\u003e\n \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen Threadripper PRO 7975WX\u003c\/td\u003e\n \u003ctd\u003e32C\/64T\u003c\/td\u003e\n \u003ctd\u003e4.00 GHz\u003c\/td\u003e\n \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen Threadripper PRO 7985WX\u003c\/td\u003e\n \u003ctd\u003e64C\/128T\u003c\/td\u003e\n \u003ctd\u003e3.20 GHz\u003c\/td\u003e\n \u003ctd\u003e5.10 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen Threadripper PRO 7995WX\u003c\/td\u003e\n \u003ctd\u003e96C\/192T\u003c\/td\u003e\n \u003ctd\u003e2.50 GHz\u003c\/td\u003e\n \u003ctd\u003e5.10 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen Threadripper PRO 9965WX\u003c\/td\u003e\n \u003ctd\u003e24C\/48T\u003c\/td\u003e\n \u003ctd\u003e4.20 GHz\u003c\/td\u003e\n \u003ctd\u003e5.40 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen Threadripper PRO 9975WX\u003c\/td\u003e\n \u003ctd\u003e32C\/64T\u003c\/td\u003e\n \u003ctd\u003e4.00 GHz\u003c\/td\u003e\n \u003ctd\u003e5.40 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen Threadripper PRO 9985WX\u003c\/td\u003e\n \u003ctd\u003e64C\/128T\u003c\/td\u003e\n \u003ctd\u003e3.20 GHz\u003c\/td\u003e\n \u003ctd\u003e5.40 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen Threadripper PRO 9995WX\u003c\/td\u003e\n \u003ctd\u003e96C\/192T\u003c\/td\u003e\n \u003ctd\u003e2.50 GHz\u003c\/td\u003e\n \u003ctd\u003e5.40 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \u003c\/div\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection gpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"gpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas Mythical Dragon\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eThe GPU is the most critical component in enterprise AI and machine learning systems, as modern workloads such as model training, fine-tuning, and high-throughput inference are fundamental to GPU acceleration. High-performance GPUs with large onboard VRAM memory enable organizations to process massive datasets, train larger and more complex models, and maintain consistent performance across production pipelines. Greater VRAM capacity allows models, datasets, and intermediate states to remain resident on the GPU, reducing data movement overhead and improving overall system efficiency fo AI and machine learning workloads.\u003c\/p\u003e\n\u003cp\u003eFor scalable AI infrastructure, deploying multiple GPUs within a single system is often essential. Parallel GPU compute allows workloads to be distributed efficiently with the usage of technologies such as CUDA, accelerating training times and increasing inference throughput for enterprise AI applications. In data center environments, sustained performance is as important as peak performance—making thermal design, power delivery, and system stability critical considerations.\u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n\n \u003cdiv class=\"table-section\"\u003e\n\n \u003cdiv class=\"gpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eGPU\u003c\/th\u003e\n \u003cth\u003eVRAM\u003c\/th\u003e\n \u003cth\u003eGPU Clock\u003c\/th\u003e\n \u003cth\u003eMemory Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Max Q Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2280 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 5000 Blackwell\u003c\/td\u003e\n \u003ctd\u003e48GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2377 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 4500 Blackwell\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA 6000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2505 MHz\u003c\/td\u003e\n \u003ctd\u003e2500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5090\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2550 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4500 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e24GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2580 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e20GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2175 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5080\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1875 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2452 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070\u003c\/td\u003e\n \u003ctd\u003e12GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2512 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5060 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2572 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A1000\u003c\/td\u003e\n \u003ctd\u003e8GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1462 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A400\u003c\/td\u003e\n \u003ctd\u003e4GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1762 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \n \u003c\/div\u003e\n \u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Mythical Dragon","offer_id":42024417656878,"sku":"CN-MYTHICAL-DRAGON-WATRU-6N4S-4G","price":2999.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Cloud_Ninjas_WATRU-4N6S-4G-6.jpg?v=1771955482"},{"product_id":"cloud-ninjas-watru-4n4s-4g-ai-workstation","title":"Shadow Leopard AI Workstation","description":"\u003cdiv class=\"subsection cpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"cpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eProcessor Specifications for Cloud Ninjas Shadow Leopard\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eCloud Ninjas Shadow Leopard focus in offering the best AMD Ryzen Threadripper processors, to allow the maximum quantity of PCIe lane availability to power AI workstations with multi-GPU capabilities and the best storage performance allowing not only SATA but also NVMe M.2 solid state drives with latest PCIe generation technology. Check below list of compatible AMD Ryzen threadripper CPUs, compare and choose the best choice for your infrastructure. \u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n \u003cdiv class=\"table-section\"\u003e\n \u003cdiv class=\"cpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eCPU\u003c\/th\u003e\n \u003cth\u003eCores \u0026amp; Threads\u003c\/th\u003e\n \u003cth\u003eBase Clock\u003c\/th\u003e\n \u003cth\u003eTurbo Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen Threadripper PRO 7965WX\u003c\/td\u003e\n \u003ctd\u003e24C\/48T\u003c\/td\u003e\n \u003ctd\u003e4.20 GHz\u003c\/td\u003e\n \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen Threadripper PRO 7975WX\u003c\/td\u003e\n \u003ctd\u003e32C\/64T\u003c\/td\u003e\n \u003ctd\u003e4.00 GHz\u003c\/td\u003e\n \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen Threadripper PRO 7985WX\u003c\/td\u003e\n \u003ctd\u003e64C\/128T\u003c\/td\u003e\n \u003ctd\u003e3.20 GHz\u003c\/td\u003e\n \u003ctd\u003e5.10 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen Threadripper PRO 7995WX\u003c\/td\u003e\n \u003ctd\u003e96C\/192T\u003c\/td\u003e\n \u003ctd\u003e2.50 GHz\u003c\/td\u003e\n \u003ctd\u003e5.10 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen Threadripper PRO 9965WX\u003c\/td\u003e\n \u003ctd\u003e24C\/48T\u003c\/td\u003e\n \u003ctd\u003e4.20 GHz\u003c\/td\u003e\n \u003ctd\u003e5.40 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen Threadripper PRO 9975WX\u003c\/td\u003e\n \u003ctd\u003e32C\/64T\u003c\/td\u003e\n \u003ctd\u003e4.00 GHz\u003c\/td\u003e\n \u003ctd\u003e5.40 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen Threadripper PRO 9985WX\u003c\/td\u003e\n \u003ctd\u003e64C\/128T\u003c\/td\u003e\n \u003ctd\u003e3.20 GHz\u003c\/td\u003e\n \u003ctd\u003e5.40 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen Threadripper PRO 9995WX\u003c\/td\u003e\n \u003ctd\u003e96C\/192T\u003c\/td\u003e\n \u003ctd\u003e2.50 GHz\u003c\/td\u003e\n \u003ctd\u003e5.40 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \u003c\/div\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection gpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"gpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas Shadow Leopard\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eThe GPU is the most critical component in enterprise AI and machine learning systems, as modern workloads such as model training, fine-tuning, and high-throughput inference are fundamental to GPU acceleration. High-performance GPUs with large onboard VRAM memory enable organizations to process massive datasets, train larger and more complex models, and maintain consistent performance across production pipelines. Greater VRAM capacity allows models, datasets, and intermediate states to remain resident on the GPU, reducing data movement overhead and improving overall system efficiency fo AI and machine learning workloads.\u003c\/p\u003e\n\u003cp\u003eFor scalable AI infrastructure, deploying multiple GPUs within a single system is often essential. Parallel GPU compute allows workloads to be distributed efficiently with the usage of technologies such as CUDA, accelerating training times and increasing inference throughput for enterprise AI applications. In data center environments, sustained performance is as important as peak performance—making thermal design, power delivery, and system stability critical considerations.\u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n\n \u003cdiv class=\"table-section\"\u003e\n\n \u003cdiv class=\"gpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eGPU\u003c\/th\u003e\n \u003cth\u003eVRAM\u003c\/th\u003e\n \u003cth\u003eGPU Clock\u003c\/th\u003e\n \u003cth\u003eMemory Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Max Q Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2280 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 5000 Blackwell\u003c\/td\u003e\n \u003ctd\u003e48GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2377 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 4500 Blackwell\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA 6000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2505 MHz\u003c\/td\u003e\n \u003ctd\u003e2500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5090\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2550 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4500 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e24GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2580 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e20GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2175 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5080\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1875 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2452 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070\u003c\/td\u003e\n \u003ctd\u003e12GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2512 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5060 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2572 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A1000\u003c\/td\u003e\n \u003ctd\u003e8GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1462 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A400\u003c\/td\u003e\n \u003ctd\u003e4GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1762 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \n \u003c\/div\u003e\n \u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Shadow Leopard","offer_id":42056864759854,"sku":"CN-SHADOW-LEOPARD-WATRU-4N4S-4G","price":2609.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Define_7_Sheetmetal_Black_XL_Left_Front_WATRU-4N4S-4G.jpg?v=1771955116"},{"product_id":"cloud-ninjas-watrg-4n8s-3g-ai-workstation","title":"Iron Bull AI Workstation","description":"\u003cdiv class=\"subsection cpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"cpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eProcessor Specifications for Cloud Ninjas Iron Bull\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eCloud Ninjas Iron Bull focus in offering the best AMD Ryzen Threadripper processors, to allow the maximum quantity of PCIe lane availability to power AI workstations with multi-GPU capabilities and the best storage performance allowing not only SATA but also NVMe M.2 solid state drives with latest PCIe generation technology. Check below list of compatible AMD Ryzen threadripper CPUs, compare and choose the best choice for your infrastructure. \u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n \u003cdiv class=\"table-section\"\u003e\n \u003cdiv class=\"cpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eCPU\u003c\/th\u003e\n \u003cth\u003eCores \u0026amp; Threads\u003c\/th\u003e\n \u003cth\u003eBase Clock\u003c\/th\u003e\n \u003cth\u003eTurbo Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen Threadripper PRO 7965WX\u003c\/td\u003e\n \u003ctd\u003e24C\/48T\u003c\/td\u003e\n \u003ctd\u003e4.20 GHz\u003c\/td\u003e\n \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen Threadripper PRO 7975WX\u003c\/td\u003e\n \u003ctd\u003e32C\/64T\u003c\/td\u003e\n \u003ctd\u003e4.00 GHz\u003c\/td\u003e\n \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen Threadripper PRO 7985WX\u003c\/td\u003e\n \u003ctd\u003e64C\/128T\u003c\/td\u003e\n \u003ctd\u003e3.20 GHz\u003c\/td\u003e\n \u003ctd\u003e5.10 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen Threadripper PRO 7995WX\u003c\/td\u003e\n \u003ctd\u003e96C\/192T\u003c\/td\u003e\n \u003ctd\u003e2.50 GHz\u003c\/td\u003e\n \u003ctd\u003e5.10 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen Threadripper PRO 9965WX\u003c\/td\u003e\n \u003ctd\u003e24C\/48T\u003c\/td\u003e\n \u003ctd\u003e4.20 GHz\u003c\/td\u003e\n \u003ctd\u003e5.40 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen Threadripper PRO 9975WX\u003c\/td\u003e\n \u003ctd\u003e32C\/64T\u003c\/td\u003e\n \u003ctd\u003e4.00 GHz\u003c\/td\u003e\n \u003ctd\u003e5.40 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen Threadripper PRO 9985WX\u003c\/td\u003e\n \u003ctd\u003e64C\/128T\u003c\/td\u003e\n \u003ctd\u003e3.20 GHz\u003c\/td\u003e\n \u003ctd\u003e5.40 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen Threadripper PRO 9995WX\u003c\/td\u003e\n \u003ctd\u003e96C\/192T\u003c\/td\u003e\n \u003ctd\u003e2.50 GHz\u003c\/td\u003e\n \u003ctd\u003e5.40 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \u003c\/div\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection gpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"gpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas Iron Bull\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eThe GPU is the most critical component in enterprise AI and machine learning projects, as modern workloads such as model training, fine-tuning and high-throughput inference are fundamental to GPU acceleration. High-performance GPUs with large onboard VRAM memory enable organizations to process massive datasets, train larger and more complex models, and maintain consistent performance across production pipelines. Greater VRAM capacity allows models, datasets, and intermediate states to remain resident on the GPU, reducing data movement overhead and improving overall system efficiency fo AI and machine learning workloads, the better the GPU the better performance related to neural matrix multiplication.\u003c\/p\u003e\n\u003cp\u003eFor scalable AI infrastructure, deploying multiple GPUs within a single system is often essential. Parallel GPU compute allows workloads to be distributed efficiently with the usage of technologies such as CUDA, accelerating training times and increasing inference throughput for enterprise AI applications. In data center environments, sustained performance is as important as peak performance—making thermal design, power delivery, and system stability critical considerations.\u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n\n \u003cdiv class=\"table-section\"\u003e\n\n \u003cdiv class=\"gpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eGPU\u003c\/th\u003e\n \u003cth\u003eVRAM\u003c\/th\u003e\n \u003cth\u003eGPU Clock\u003c\/th\u003e\n \u003cth\u003eMemory Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Max Q Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2280 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 5000 Blackwell\u003c\/td\u003e\n \u003ctd\u003e48GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2377 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 4500 Blackwell\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA 6000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2505 MHz\u003c\/td\u003e\n \u003ctd\u003e2500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5090\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2550 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4500 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e24GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2580 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e20GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2175 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5080\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1875 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2452 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070\u003c\/td\u003e\n \u003ctd\u003e12GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2512 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5060 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2572 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A1000\u003c\/td\u003e\n \u003ctd\u003e8GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1462 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A400\u003c\/td\u003e\n \u003ctd\u003e4GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1762 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \n \u003c\/div\u003e\n \u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Iron Bull","offer_id":42058334076974,"sku":"CN-Iron-Bull-WATRG-4N8S-3G","price":2139.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Define_7-WATRG-4N8S-3G-8.jpg?v=1771955073"},{"product_id":"cloud-ninjas-waru-2n2s-1g-ai-workstation","title":"Phantom Lynx AI Workstation","description":"\u003cdiv class=\"subsection cpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"cpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eProcessor Specifications for Cloud Ninjas Phantom Lynx\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eCloud Ninjas Phantom Lynx focus in offering the best AMD Ryzen series processors together with the best possible GPUs that can fit on this Mini ATX or ITX case. Together with two NVMe M.2 and 2 SATA solid state drives, the Phantom Lynx is the perfect solution for a small form factor system that runs any AI Model, heavy 3D modeling, video editing, and other advanced workstation workloads. Built for users who need high performance workstation at smaller physical sizes. \u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n \u003cdiv class=\"table-section\"\u003e\n \u003cdiv class=\"cpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eCPU\u003c\/th\u003e\n \u003cth\u003eCores \u0026amp; Threads\u003c\/th\u003e\n \u003cth\u003eBase Clock\u003c\/th\u003e\n \u003cth\u003eTurbo Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen 7 9700X\u003c\/td\u003e\n \u003ctd\u003e8C\/16T\u003c\/td\u003e\n \u003ctd\u003e3.80 GHz\u003c\/td\u003e\n \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen 7 9800X3D\u003c\/td\u003e\n \u003ctd\u003e8C\/16T\u003c\/td\u003e\n \u003ctd\u003e4.70 GHz\u003c\/td\u003e\n \u003ctd\u003e5.20 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen 9 9900X\u003c\/td\u003e\n \u003ctd\u003e12C\/24T\u003c\/td\u003e\n \u003ctd\u003e4.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.60 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen 9 9900X3D\u003c\/td\u003e\n \u003ctd\u003e12C\/24T\u003c\/td\u003e\n \u003ctd\u003e4.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen 9 9950X\u003c\/td\u003e\n \u003ctd\u003e16C\/32T\u003c\/td\u003e\n \u003ctd\u003e4.30 GHz\u003c\/td\u003e\n \u003ctd\u003e5.70 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen 9 9950X3D\u003c\/td\u003e\n \u003ctd\u003e16C\/32T\u003c\/td\u003e\n \u003ctd\u003e4.30 GHz\u003c\/td\u003e\n \u003ctd\u003e5.70 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \u003c\/div\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection gpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"gpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas Phantom Lynx\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \t\u003cp\u003eIn machine learning, AI, video editing, 3D Modeling among other heavy workloads, the GPU remains the most critical performance component, as modern workloads such as model training, fine-tuning and inference rely on GPU accelaration. A single high-performance consumer or enterprise GPU with ample VRAM enables efficient processing of large datasets, execution of complex models, and consistent performance across development and production workflows. High speeds for video editing, and 3D modeling renderization with high VRAM GPUs can be achieved at the highest performance. Fo Ai and machine learning, higher VRAM capacity allows models, data and tensors to remain in the GPU, reducing memory transfers and latency to improve overall performance in matrix and neural network computations.\n\u003c\/p\u003e\n\n\u003cp\u003eFor space-constrained and edge-focused AI infra, carefully selecting a single-GPU configuration can deliver an optimal balance of performance, and system-level optimization. Since chassi size is a constraint in number of possible GPUs, this system values sustainability in performance computation through proper cooling solutions, reliable power delivery, and system-level optimization. In enterprise and preofessional environments, consistent performance over long time periods is essential. And the Cloud Ninjas Phantom Lynx achieves just that perfectly. \u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n\n \u003cdiv class=\"table-section\"\u003e\n\n \u003cdiv class=\"gpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eGPU\u003c\/th\u003e\n \u003cth\u003eVRAM\u003c\/th\u003e\n \u003cth\u003eGPU Clock\u003c\/th\u003e\n \u003cth\u003eMemory Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Max Q Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2280 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 5000 Blackwell\u003c\/td\u003e\n \u003ctd\u003e48GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2377 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 4500 Blackwell\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA 6000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2505 MHz\u003c\/td\u003e\n \u003ctd\u003e2500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5090\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2550 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4500 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e24GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2580 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e20GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2175 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5080\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1875 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2452 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070\u003c\/td\u003e\n \u003ctd\u003e12GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2512 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5060 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2572 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A1000\u003c\/td\u003e\n \u003ctd\u003e8GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1462 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A400\u003c\/td\u003e\n \u003ctd\u003e4GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1762 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \n \u003c\/div\u003e\n \u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Phantom Lynx Liquid Cooled","offer_id":42062828339246,"sku":"CN-PHANTOM-LYNX-WARU-2N2S-1G-LQC","price":1249.99,"currency_code":"USD","in_stock":true},{"title":"Cloud Ninjas Phantom Lynx Air Cooled","offer_id":42062831779886,"sku":"CN-PHANTOM-LYNX-WARU-2N2S-1G-AC","price":1299.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Define_7_Mini-WARU-2N2S-1G-1-LQC.jpg?v=1771955062"},{"product_id":"cloud-ninjas-wiw35u-5n6s-4g-ai-workstation","title":"Primal Gorilla AI Workstation","description":"\u003cdiv class=\"subsection cpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"cpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eProcessor Specifications for Cloud Ninjas Primal Gorilla\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eCloud Ninjas Primal Gorilla focus in offering the best Intel Xeon W series processors together with the best possible GPUs for heavy AI\/ML workloads on a workstation form factor. With 3 NVMe M.2, 2 NVMe U.2 and 2 SATA solid state drives, the Primal Gorilla is the perfect solution for a large form factor system that runs any AI Model, heavy 3D modeling, video editing, and other advanced workstation workloads. Built for users who need high performance workstations in a workstation form factor on premise, rather than stored at data centers. \u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n \u003cdiv class=\"table-section\"\u003e\n \u003cdiv class=\"cpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eCPU\u003c\/th\u003e\n \u003cth\u003eCores \u0026amp; Threads\u003c\/th\u003e\n \u003cth\u003eBase Clock\u003c\/th\u003e\n \u003cth\u003eTurbo Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Xeon w5-3525\u003c\/td\u003e\n \u003ctd\u003e16C\/32T\u003c\/td\u003e\n \u003ctd\u003e3.20 GHz\u003c\/td\u003e\n \u003ctd\u003e4.80 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Xeon w5-3535X\u003c\/td\u003e\n \u003ctd\u003e20C\/40T\u003c\/td\u003e\n \u003ctd\u003e2.90 GHz\u003c\/td\u003e\n \u003ctd\u003e4.80 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Xeon w7-3545\u003c\/td\u003e\n \u003ctd\u003e24C\/48T\u003c\/td\u003e\n \u003ctd\u003e2.70 GHz\u003c\/td\u003e\n \u003ctd\u003e4.80 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Xeon w7-3555\u003c\/td\u003e\n \u003ctd\u003e28C\/56T\u003c\/td\u003e\n \u003ctd\u003e2.70 GHz\u003c\/td\u003e\n \u003ctd\u003e4.80 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Xeon w7-3565X\u003c\/td\u003e\n \u003ctd\u003e32C\/64T\u003c\/td\u003e\n \u003ctd\u003e2.50 GHz\u003c\/td\u003e\n \u003ctd\u003e4.80 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Xeon w9-3575X\u003c\/td\u003e\n \u003ctd\u003e44C\/88T\u003c\/td\u003e\n \u003ctd\u003e2.20 GHz\u003c\/td\u003e\n \u003ctd\u003e4.80 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Xeon w9-3595X\u003c\/td\u003e\n \u003ctd\u003e60C\/120T\u003c\/td\u003e\n \u003ctd\u003e2.00 GHz\u003c\/td\u003e\n \u003ctd\u003e4.80 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \u003c\/div\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection gpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"gpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas Primal Gorilla\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \t\u003cp\u003eIn machine learning, AI, video editing, 3D Modeling among other heavy workloads, the GPU remains the most critical performance component, as modern workloads such as model training, fine-tuning and inference rely on GPU accelaration. A multiple high-performance consumer or enterprise GPU infrastructure with ample VRAM enables high efficiency processing of large datasets, execution of complex models, and consistent performance across development and production workflows. High speeds for video editing, and 3D modeling renderization with high VRAM GPUs can be achieved at the highest performance. For AI and machine learning, higher VRAM capacity allows models, data and tensors to remain in the GPU, reducing memory transfers and latency to improve overall performance in matrix and neural network computations, hence the Cloud Ninjas Primal Gorilla, offers the best non data-center platform for AI workloads, by offering up to 4 consumer or enterprise GPUs.\n\u003c\/p\u003e\n\n\u003cp\u003eFor space-constrained and edge-focused AI infrastructure, carefully selecting a Multi-GPU configuration can deliver extreme top performance, and system-level optimization. This system values speed and performance computation through proper cooling solutions, reliable power delivery, and system-level optimization. In enterprise and professional environments, consistent performance over long time periods is essential. And the Cloud Ninjas Primal Gorilla achieves just that perfectly. \u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n\n \u003cdiv class=\"table-section\"\u003e\n\n \u003cdiv class=\"gpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eGPU\u003c\/th\u003e\n \u003cth\u003eVRAM\u003c\/th\u003e\n \u003cth\u003eGPU Clock\u003c\/th\u003e\n \u003cth\u003eMemory Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Max Q Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2280 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 5000 Blackwell\u003c\/td\u003e\n \u003ctd\u003e48GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2377 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 4500 Blackwell\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA 6000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2505 MHz\u003c\/td\u003e\n \u003ctd\u003e2500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5090\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2550 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4500 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e24GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2580 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e20GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2175 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5080\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1875 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2452 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070\u003c\/td\u003e\n \u003ctd\u003e12GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2512 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5060 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2572 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A1000\u003c\/td\u003e\n \u003ctd\u003e8GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1462 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A400\u003c\/td\u003e\n \u003ctd\u003e4GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1762 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \n \u003c\/div\u003e\n \u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Primal Gorilla","offer_id":42069146796078,"sku":"CN-PRIMAL-GORILLA-WIW35U-5N6S-4G","price":2669.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Define_7_XL-WIW35U-5N6S-4G-1.jpg?v=1771955570"},{"product_id":"cloud-ninjas-waeg-5n6s-4g-ai-workstation","title":"Rhino Juggernaut AI Workstation","description":"\u003cdiv class=\"subsection cpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"cpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eProcessor Specifications for Cloud Ninjas Rhino Juggernaut\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eCloud Ninjas Rhino Juggernaut focus in offering the best enterprise grade workstation with AMD EPYC processors together with the best possible GPUs for heavy AI\/ML workloads. With 1 NVMe M.2, 4 NVMe U.2 and 6 SATA solid state drives, the Rhino Juggernaut is the perfect solution for a large form factor workstation that runs any AI Model, heavy 3D modeling, video editing, and other advanced workloads on an enterprise data center level. Built for users who need high performance data center components in a workstation form factor on premise, rather than stored at data centers. \u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n \u003cdiv class=\"table-section\"\u003e\n \u003cdiv class=\"cpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eCPU\u003c\/th\u003e\n \u003cth\u003eCores \u0026amp; Threads\u003c\/th\u003e\n \u003cth\u003eBase Clock\u003c\/th\u003e\n \u003cth\u003eTurbo Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9015\u003c\/td\u003e\n \u003ctd\u003e8C\/16T\u003c\/td\u003e\n \u003ctd\u003e3.60GHz\u003c\/td\u003e\n \u003ctd\u003e4.10GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9115\u003c\/td\u003e\n \u003ctd\u003e16C\/32T\u003c\/td\u003e\n \u003ctd\u003e2.60GHz\u003c\/td\u003e\n \u003ctd\u003e4.10GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9135\u003c\/td\u003e\n \u003ctd\u003e16C\/32T\u003c\/td\u003e\n \u003ctd\u003e3.65GHz\u003c\/td\u003e\n \u003ctd\u003e4.30GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9175F \u003c\/td\u003e\n \u003ctd\u003e16C\/32T\u003c\/td\u003e\n \u003ctd\u003e4.20GHz\u003c\/td\u003e\n \u003ctd\u003e5.00GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9255\u003c\/td\u003e\n \u003ctd\u003e24C\/48T\u003c\/td\u003e\n \u003ctd\u003e3.25GHz\u003c\/td\u003e\n \u003ctd\u003e4.80GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9275F\u003c\/td\u003e\n \u003ctd\u003e24C\/48T\u003c\/td\u003e\n \u003ctd\u003e4.10GHz\u003c\/td\u003e\n \u003ctd\u003e4.80GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9335\u003c\/td\u003e\n \u003ctd\u003e32C\/ 64T\u003c\/td\u003e\n \u003ctd\u003e3.00GHz\u003c\/td\u003e\n \u003ctd\u003e4.40GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9355\u003c\/td\u003e\n \u003ctd\u003e32C\/64T\u003c\/td\u003e\n \u003ctd\u003e3.55GHz\u003c\/td\u003e\n \u003ctd\u003e4.40GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctd\u003eAMD EPYC 9355P\u003c\/td\u003e\n \u003ctd\u003e32C\/64T\u003c\/td\u003e\n \u003ctd\u003e3.55GHz\u003c\/td\u003e\n \u003ctd\u003e4.40GHz\u003c\/td\u003e\n \n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9365\u003c\/td\u003e\n \u003ctd\u003e36C\/72T\u003c\/td\u003e\n \u003ctd\u003e3.40GHz\u003c\/td\u003e\n \u003ctd\u003e4.30GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9375F\u003c\/td\u003e\n \u003ctd\u003e32C\/64T\u003c\/td\u003e\n \u003ctd\u003e3.85GHz\u003c\/td\u003e\n \u003ctd\u003e4.80GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9455\u003c\/td\u003e\n \u003ctd\u003e48C\/96T\u003c\/td\u003e\n \u003ctd\u003e3.15GHz\u003c\/td\u003e\n \u003ctd\u003e4.40GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9455P\u003c\/td\u003e\n \u003ctd\u003e48C\/96T\u003c\/td\u003e\n \u003ctd\u003e3.15GHz\u003c\/td\u003e\n \u003ctd\u003e4.40GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9475F\u003c\/td\u003e\n \u003ctd\u003e48C\/96T\u003c\/td\u003e\n \u003ctd\u003e3.65GHz\u003c\/td\u003e\n \u003ctd\u003e4.80GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9535\u003c\/td\u003e\n \u003ctd\u003e64C\/128T\u003c\/td\u003e\n \u003ctd\u003e2.40GHz\u003c\/td\u003e\n \u003ctd\u003e4.30GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9535\u003c\/td\u003e\n \u003ctd\u003e64C\/128T\u003c\/td\u003e\n \u003ctd\u003e2.40GHz\u003c\/td\u003e\n \u003ctd\u003e4.30GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9555\u003c\/td\u003e\n \u003ctd\u003e64C\/128T\u003c\/td\u003e\n \u003ctd\u003e3.20GHz\u003c\/td\u003e\n \u003ctd\u003e4.40GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9555P\u003c\/td\u003e\n \u003ctd\u003e64C\/128T\u003c\/td\u003e\n \u003ctd\u003e3.20GHz\u003c\/td\u003e\n \u003ctd\u003e4.40GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9565\u003c\/td\u003e\n \u003ctd\u003e72C\/144T\u003c\/td\u003e\n \u003ctd\u003e3.15GHz\u003c\/td\u003e\n \u003ctd\u003e4.30GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9575F\u003c\/td\u003e\n \u003ctd\u003e64C\/128T\u003c\/td\u003e\n \u003ctd\u003e3.30GHz\u003c\/td\u003e\n \u003ctd\u003e5.00GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9645\u003c\/td\u003e\n \u003ctd\u003e96C\/192T\u003c\/td\u003e\n \u003ctd\u003e2.3GHz\u003c\/td\u003e\n \u003ctd\u003e3.70GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9655\u003c\/td\u003e\n \u003ctd\u003e96C\/192T\u003c\/td\u003e\n \u003ctd\u003e2.60GHz\u003c\/td\u003e\n \u003ctd\u003e4.50GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9655P\u003c\/td\u003e\n \u003ctd\u003e96C\/192T\u003c\/td\u003e\n \u003ctd\u003e2.60GHz\u003c\/td\u003e\n \u003ctd\u003e4.50GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9745\u003c\/td\u003e\n \u003ctd\u003e128C\/256T\u003c\/td\u003e\n \u003ctd\u003e2.40GHz\u003c\/td\u003e\n \u003ctd\u003e3.70GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9755\u003c\/td\u003e\n \u003ctd\u003e128C\/256T\u003c\/td\u003e\n \u003ctd\u003e2.70GHz\u003c\/td\u003e\n \u003ctd\u003e4.10GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \u003c\/div\u003e\n\n \u003cdiv class=\"table-section\"\u003e\n \u003cdiv class=\"cpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eCPU\u003c\/th\u003e\n \u003cth\u003eCores \u0026amp; Threads\u003c\/th\u003e\n \u003cth\u003eBase Clock\u003c\/th\u003e\n \u003cth\u003eTurbo Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9124\u003c\/td\u003e\n \u003ctd\u003e16C\/32T\u003c\/td\u003e\n \u003ctd\u003e3.00GHz\u003c\/td\u003e\n \u003ctd\u003e3.70GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9174F\u003c\/td\u003e\n \u003ctd\u003e16C\/32T\u003c\/td\u003e\n \u003ctd\u003e4.10GHz\u003c\/td\u003e\n \u003ctd\u003e4.40GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9184X\u003c\/td\u003e\n \u003ctd\u003e16C\/32T\u003c\/td\u003e\n \u003ctd\u003e3.55GHz\u003c\/td\u003e\n \u003ctd\u003e4.10GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9224\u003c\/td\u003e\n \u003ctd\u003e24C\/48T\u003c\/td\u003e\n \u003ctd\u003e2.50GHz\u003c\/td\u003e\n \u003ctd\u003e3.70GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9254\u003c\/td\u003e\n \u003ctd\u003e24C\/48T\u003c\/td\u003e\n \u003ctd\u003e2.96GHz\u003c\/td\u003e\n \u003ctd\u003e4.15GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9274F\u003c\/td\u003e\n \u003ctd\u003e24C\/48T\u003c\/td\u003e\n \u003ctd\u003e4.05.10GHz\u003c\/td\u003e\n \u003ctd\u003e4.30GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9334\u003c\/td\u003e\n \u003ctd\u003e32C\/64T\u003c\/td\u003e\n \u003ctd\u003e2.70GHz\u003c\/td\u003e\n \u003ctd\u003e3.90GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9354\u003c\/td\u003e\n \u003ctd\u003e32C\/64T\u003c\/td\u003e\n \u003ctd\u003e3.25GHz\u003c\/td\u003e\n \u003ctd\u003e3.80GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9354P\u003c\/td\u003e\n \u003ctd\u003e32C\/64T\u003c\/td\u003e\n \u003ctd\u003e3.25GHz\u003c\/td\u003e\n \u003ctd\u003e3.80GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9374F\u003c\/td\u003e\n \u003ctd\u003e32C\/64T\u003c\/td\u003e\n \u003ctd\u003e3.85GHz\u003c\/td\u003e\n \u003ctd\u003e4.30GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9384X\u003c\/td\u003e\n \u003ctd\u003e32C\/64T\u003c\/td\u003e\n \u003ctd\u003e3.10GHz\u003c\/td\u003e \n \u003ctd\u003e3.90GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9454\u003c\/td\u003e\n \u003ctd\u003e48C\/96T\u003c\/td\u003e\n \u003ctd\u003e2.75GHz\u003c\/td\u003e\n \u003ctd\u003e3.80GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9454P\u003c\/td\u003e\n \u003ctd\u003e48C\/96T\u003c\/td\u003e\n \u003ctd\u003e2.75GHz\u003c\/td\u003e\n \u003ctd\u003e3.90GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9474F\u003c\/td\u003e\n \u003ctd\u003e48C\/96T\u003c\/td\u003e\n \u003ctd\u003e3.60GHz\u003c\/td\u003e\n \u003ctd\u003e4.10GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9534\u003c\/td\u003e\n \u003ctd\u003e64C\/128T\u003c\/td\u003e\n \u003ctd\u003e2.45GHz\u003c\/td\u003e\n \u003ctd\u003e3.70GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9554 \u003c\/td\u003e\n \u003ctd\u003e64C\/128T\u003c\/td\u003e\n \u003ctd\u003e3.10GHz\u003c\/td\u003e\n \u003ctd\u003e3.75GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9554P\u003c\/td\u003e\n \u003ctd\u003e64C\/128T\u003c\/td\u003e\n \u003ctd\u003e3.10GHz\u003c\/td\u003e\n \u003ctd\u003e3.75GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9634\u003c\/td\u003e\n \u003ctd\u003e84C\/168T\u003c\/td\u003e\n \u003ctd\u003e2.25GHz\u003c\/td\u003e\n \u003ctd\u003e3.70GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9654\u003c\/td\u003e\n \u003ctd\u003e96C\/192T\u003c\/td\u003e\n \u003ctd\u003e2.40GHz\u003c\/td\u003e\n \u003ctd\u003e3.70GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9654P\u003c\/td\u003e\n \u003ctd\u003e96C\/192T\u003c\/td\u003e\n \u003ctd\u003e2.40GHz\u003c\/td\u003e\n \u003ctd\u003e3.70GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9684X\u003c\/td\u003e\n \u003ctd\u003e96C\/192T\u003c\/td\u003e\n \u003ctd\u003e2.55GHz\u003c\/td\u003e\n \u003ctd\u003e3.70GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9734\u003c\/td\u003e\n \u003ctd\u003e112C\/224T\u003c\/td\u003e\n \u003ctd\u003e2.20GHz\u003c\/td\u003e\n \u003ctd\u003e3.00GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9754\u003c\/td\u003e\n \u003ctd\u003e128C\/256T\u003c\/td\u003e\n \u003ctd\u003e2.25GHz\u003c\/td\u003e\n \u003ctd\u003e3.10GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \u003c\/div\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection gpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"gpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas Rhino Juggernaut\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \t\u003cp\u003eIn machine learning, AI, video editing, 3D Modeling among other heavy workloads, the GPU remains the most critical performance component, as modern workloads such as model training, fine-tuning and inference rely on GPU accelaration. A multiple high-performance consumer or enterprise GPU infrastructure with ample VRAM enables high efficiency processing of large datasets, execution of complex models, and consistent performance across development and production workflows. High speeds for video editing, and 3D modeling renderization with high VRAM GPUs can be achieved at the highest performance. For AI and machine learning, higher VRAM capacity allows models, data and tensors to remain in the GPU, reducing memory transfers and latency to improve overall performance in matrix and neural network computations, hence the Cloud Ninjas Rhino Juggernaut, offers the best non data-center platform for AI workloads, by offering up to 4 consumer or enterprise GPUs.\n\u003c\/p\u003e\n\n\u003cp\u003eFor space-constrained and edge-focused AI infrastructure, carefully selecting a Multi-GPU configuration can deliver extreme top performance, and system-level optimization. This system values speed and performance computation through proper cooling solutions, reliable power delivery, and system-level optimization. In enterprise and professional environments, consistent performance over long time periods is essential. And the Cloud Ninjas Rhino Juggernaut achieves just that perfectly. \u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n\n \u003cdiv class=\"table-section\"\u003e\n\n \u003cdiv class=\"gpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eGPU\u003c\/th\u003e\n \u003cth\u003eVRAM\u003c\/th\u003e\n \u003cth\u003eGPU Clock\u003c\/th\u003e\n \u003cth\u003eMemory Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Max Q Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2280 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 5000 Blackwell\u003c\/td\u003e\n \u003ctd\u003e48GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2377 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 4500 Blackwell\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA 6000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2505 MHz\u003c\/td\u003e\n \u003ctd\u003e2500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5090\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2550 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4500 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e24GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2580 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e20GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2175 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5080\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1875 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2452 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070\u003c\/td\u003e\n \u003ctd\u003e12GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2512 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5060 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2572 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A1000\u003c\/td\u003e\n \u003ctd\u003e8GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1462 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A400\u003c\/td\u003e\n \u003ctd\u003e4GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1762 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \n \u003c\/div\u003e\n \u003c\/div\u003e\n\u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Rhino Juggernaut","offer_id":42070736830510,"sku":"CN-RHINO-JUGGERNAUT-WAEG-5N6S-4G","price":3609.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Define_7_XL-WAEG-5N6S-4G-1.jpg?v=1771955579"},{"product_id":"cloud-ninjas-waeg-5n4s-4g-ai-workstation","title":"Fierce Tiger AI Workstation","description":"\u003cdiv class=\"subsection cpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"cpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eProcessor Specifications for Cloud Ninjas Fierce Tige\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eCloud Ninjas Fierce Tige focus in offering the best enterprise grade workstation with AMD EPYC processors together with the best possible GPUs for heavy AI\/ML workloads. With 1 NVMe M.2, 4 NVMe U.2 and 4 SATA solid state drives, the Fierce Tige is the perfect solution for a large form factor workstation that runs any AI Model, heavy 3D modeling, video editing, and other advanced workloads on an enterprise data center level. Built for users who need high performance data center components in a workstation form factor on premise, rather than stored at data centers. \u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n \u003cdiv class=\"table-section\"\u003e\n \u003cdiv class=\"cpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eCPU\u003c\/th\u003e\n \u003cth\u003eCores \u0026amp; Threads\u003c\/th\u003e\n \u003cth\u003eBase Clock\u003c\/th\u003e\n \u003cth\u003eTurbo Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9015\u003c\/td\u003e\n \u003ctd\u003e8C\/16T\u003c\/td\u003e\n \u003ctd\u003e3.60GHz\u003c\/td\u003e\n \u003ctd\u003e4.10GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9115\u003c\/td\u003e\n \u003ctd\u003e16C\/32T\u003c\/td\u003e\n \u003ctd\u003e2.60GHz\u003c\/td\u003e\n \u003ctd\u003e4.10GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9135\u003c\/td\u003e\n \u003ctd\u003e16C\/32T\u003c\/td\u003e\n \u003ctd\u003e3.65GHz\u003c\/td\u003e\n \u003ctd\u003e4.30GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9175F \u003c\/td\u003e\n \u003ctd\u003e16C\/32T\u003c\/td\u003e\n \u003ctd\u003e4.20GHz\u003c\/td\u003e\n \u003ctd\u003e5.00GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9255\u003c\/td\u003e\n \u003ctd\u003e24C\/48T\u003c\/td\u003e\n \u003ctd\u003e3.25GHz\u003c\/td\u003e\n \u003ctd\u003e4.80GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9275F\u003c\/td\u003e\n \u003ctd\u003e24C\/48T\u003c\/td\u003e\n \u003ctd\u003e4.10GHz\u003c\/td\u003e\n \u003ctd\u003e4.80GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9335\u003c\/td\u003e\n \u003ctd\u003e32C\/ 64T\u003c\/td\u003e\n \u003ctd\u003e3.00GHz\u003c\/td\u003e\n \u003ctd\u003e4.40GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9355\u003c\/td\u003e\n \u003ctd\u003e32C\/64T\u003c\/td\u003e\n \u003ctd\u003e3.55GHz\u003c\/td\u003e\n \u003ctd\u003e4.40GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctd\u003eAMD EPYC 9355P\u003c\/td\u003e\n \u003ctd\u003e32C\/64T\u003c\/td\u003e\n \u003ctd\u003e3.55GHz\u003c\/td\u003e\n \u003ctd\u003e4.40GHz\u003c\/td\u003e\n \n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9365\u003c\/td\u003e\n \u003ctd\u003e36C\/72T\u003c\/td\u003e\n \u003ctd\u003e3.40GHz\u003c\/td\u003e\n \u003ctd\u003e4.30GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9375F\u003c\/td\u003e\n \u003ctd\u003e32C\/64T\u003c\/td\u003e\n \u003ctd\u003e3.85GHz\u003c\/td\u003e\n \u003ctd\u003e4.80GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9455\u003c\/td\u003e\n \u003ctd\u003e48C\/96T\u003c\/td\u003e\n \u003ctd\u003e3.15GHz\u003c\/td\u003e\n \u003ctd\u003e4.40GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9455P\u003c\/td\u003e\n \u003ctd\u003e48C\/96T\u003c\/td\u003e\n \u003ctd\u003e3.15GHz\u003c\/td\u003e\n \u003ctd\u003e4.40GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9475F\u003c\/td\u003e\n \u003ctd\u003e48C\/96T\u003c\/td\u003e\n \u003ctd\u003e3.65GHz\u003c\/td\u003e\n \u003ctd\u003e4.80GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9535\u003c\/td\u003e\n \u003ctd\u003e64C\/128T\u003c\/td\u003e\n \u003ctd\u003e2.40GHz\u003c\/td\u003e\n \u003ctd\u003e4.30GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9535\u003c\/td\u003e\n \u003ctd\u003e64C\/128T\u003c\/td\u003e\n \u003ctd\u003e2.40GHz\u003c\/td\u003e\n \u003ctd\u003e4.30GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9555\u003c\/td\u003e\n \u003ctd\u003e64C\/128T\u003c\/td\u003e\n \u003ctd\u003e3.20GHz\u003c\/td\u003e\n \u003ctd\u003e4.40GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9555P\u003c\/td\u003e\n \u003ctd\u003e64C\/128T\u003c\/td\u003e\n \u003ctd\u003e3.20GHz\u003c\/td\u003e\n \u003ctd\u003e4.40GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9565\u003c\/td\u003e\n \u003ctd\u003e72C\/144T\u003c\/td\u003e\n \u003ctd\u003e3.15GHz\u003c\/td\u003e\n \u003ctd\u003e4.30GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9575F\u003c\/td\u003e\n \u003ctd\u003e64C\/128T\u003c\/td\u003e\n \u003ctd\u003e3.30GHz\u003c\/td\u003e\n \u003ctd\u003e5.00GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9645\u003c\/td\u003e\n \u003ctd\u003e96C\/192T\u003c\/td\u003e\n \u003ctd\u003e2.3GHz\u003c\/td\u003e\n \u003ctd\u003e3.70GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9655\u003c\/td\u003e\n \u003ctd\u003e96C\/192T\u003c\/td\u003e\n \u003ctd\u003e2.60GHz\u003c\/td\u003e\n \u003ctd\u003e4.50GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9655P\u003c\/td\u003e\n \u003ctd\u003e96C\/192T\u003c\/td\u003e\n \u003ctd\u003e2.60GHz\u003c\/td\u003e\n \u003ctd\u003e4.50GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9745\u003c\/td\u003e\n \u003ctd\u003e128C\/256T\u003c\/td\u003e\n \u003ctd\u003e2.40GHz\u003c\/td\u003e\n \u003ctd\u003e3.70GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9755\u003c\/td\u003e\n \u003ctd\u003e128C\/256T\u003c\/td\u003e\n \u003ctd\u003e2.70GHz\u003c\/td\u003e\n \u003ctd\u003e4.10GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \u003c\/div\u003e\n\n \u003cdiv class=\"table-section\"\u003e\n \u003cdiv class=\"cpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eCPU\u003c\/th\u003e\n \u003cth\u003eCores \u0026amp; Threads\u003c\/th\u003e\n \u003cth\u003eBase Clock\u003c\/th\u003e\n \u003cth\u003eTurbo Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9124\u003c\/td\u003e\n \u003ctd\u003e16C\/32T\u003c\/td\u003e\n \u003ctd\u003e3.00GHz\u003c\/td\u003e\n \u003ctd\u003e3.70GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9174F\u003c\/td\u003e\n \u003ctd\u003e16C\/32T\u003c\/td\u003e\n \u003ctd\u003e4.10GHz\u003c\/td\u003e\n \u003ctd\u003e4.40GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9184X\u003c\/td\u003e\n \u003ctd\u003e16C\/32T\u003c\/td\u003e\n \u003ctd\u003e3.55GHz\u003c\/td\u003e\n \u003ctd\u003e4.10GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9224\u003c\/td\u003e\n \u003ctd\u003e24C\/48T\u003c\/td\u003e\n \u003ctd\u003e2.50GHz\u003c\/td\u003e\n \u003ctd\u003e3.70GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9254\u003c\/td\u003e\n \u003ctd\u003e24C\/48T\u003c\/td\u003e\n \u003ctd\u003e2.96GHz\u003c\/td\u003e\n \u003ctd\u003e4.15GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9274F\u003c\/td\u003e\n \u003ctd\u003e24C\/48T\u003c\/td\u003e\n \u003ctd\u003e4.05.10GHz\u003c\/td\u003e\n \u003ctd\u003e4.30GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9334\u003c\/td\u003e\n \u003ctd\u003e32C\/64T\u003c\/td\u003e\n \u003ctd\u003e2.70GHz\u003c\/td\u003e\n \u003ctd\u003e3.90GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9354\u003c\/td\u003e\n \u003ctd\u003e32C\/64T\u003c\/td\u003e\n \u003ctd\u003e3.25GHz\u003c\/td\u003e\n \u003ctd\u003e3.80GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9354P\u003c\/td\u003e\n \u003ctd\u003e32C\/64T\u003c\/td\u003e\n \u003ctd\u003e3.25GHz\u003c\/td\u003e\n \u003ctd\u003e3.80GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9374F\u003c\/td\u003e\n \u003ctd\u003e32C\/64T\u003c\/td\u003e\n \u003ctd\u003e3.85GHz\u003c\/td\u003e\n \u003ctd\u003e4.30GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9384X\u003c\/td\u003e\n \u003ctd\u003e32C\/64T\u003c\/td\u003e\n \u003ctd\u003e3.10GHz\u003c\/td\u003e \n \u003ctd\u003e3.90GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9454\u003c\/td\u003e\n \u003ctd\u003e48C\/96T\u003c\/td\u003e\n \u003ctd\u003e2.75GHz\u003c\/td\u003e\n \u003ctd\u003e3.80GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9454P\u003c\/td\u003e\n \u003ctd\u003e48C\/96T\u003c\/td\u003e\n \u003ctd\u003e2.75GHz\u003c\/td\u003e\n \u003ctd\u003e3.90GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9474F\u003c\/td\u003e\n \u003ctd\u003e48C\/96T\u003c\/td\u003e\n \u003ctd\u003e3.60GHz\u003c\/td\u003e\n \u003ctd\u003e4.10GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9534\u003c\/td\u003e\n \u003ctd\u003e64C\/128T\u003c\/td\u003e\n \u003ctd\u003e2.45GHz\u003c\/td\u003e\n \u003ctd\u003e3.70GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9554 \u003c\/td\u003e\n \u003ctd\u003e64C\/128T\u003c\/td\u003e\n \u003ctd\u003e3.10GHz\u003c\/td\u003e\n \u003ctd\u003e3.75GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9554P\u003c\/td\u003e\n \u003ctd\u003e64C\/128T\u003c\/td\u003e\n \u003ctd\u003e3.10GHz\u003c\/td\u003e\n \u003ctd\u003e3.75GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9634\u003c\/td\u003e\n \u003ctd\u003e84C\/168T\u003c\/td\u003e\n \u003ctd\u003e2.25GHz\u003c\/td\u003e\n \u003ctd\u003e3.70GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9654\u003c\/td\u003e\n \u003ctd\u003e96C\/192T\u003c\/td\u003e\n \u003ctd\u003e2.40GHz\u003c\/td\u003e\n \u003ctd\u003e3.70GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9654P\u003c\/td\u003e\n \u003ctd\u003e96C\/192T\u003c\/td\u003e\n \u003ctd\u003e2.40GHz\u003c\/td\u003e\n \u003ctd\u003e3.70GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9684X\u003c\/td\u003e\n \u003ctd\u003e96C\/192T\u003c\/td\u003e\n \u003ctd\u003e2.55GHz\u003c\/td\u003e\n \u003ctd\u003e3.70GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9734\u003c\/td\u003e\n \u003ctd\u003e112C\/224T\u003c\/td\u003e\n \u003ctd\u003e2.20GHz\u003c\/td\u003e\n \u003ctd\u003e3.00GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD EPYC 9754\u003c\/td\u003e\n \u003ctd\u003e128C\/256T\u003c\/td\u003e\n \u003ctd\u003e2.25GHz\u003c\/td\u003e\n \u003ctd\u003e3.10GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \u003c\/div\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection gpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"gpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas Fierce Tige\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \t\u003cp\u003eIn machine learning, AI, video editing, 3D Modeling among other heavy workloads, the GPU remains the most critical performance component, as modern workloads such as model training, fine-tuning and inference rely on GPU accelaration. A multiple high-performance consumer or enterprise GPU infrastructure with ample VRAM enables high efficiency processing of large datasets, execution of complex models, and consistent performance across development and production workflows. High speeds for video editing, and 3D modeling renderization with high VRAM GPUs can be achieved at the highest performance. For AI and machine learning, higher VRAM capacity allows models, data and tensors to remain in the GPU, reducing memory transfers and latency to improve overall performance in matrix and neural network computations, hence the Cloud Ninjas Fierce Tige, offers the best non data-center platform for AI workloads, by offering up to 4 consumer or enterprise GPUs.\n\u003c\/p\u003e\n\n\u003cp\u003eFor space-constrained and edge-focused AI infrastructure, carefully selecting a Multi-GPU configuration can deliver extreme top performance, and system-level optimization. This system values speed and performance computation through proper cooling solutions, reliable power delivery, and system-level optimization. In enterprise and professional environments, consistent performance over long time periods is essential. And the Cloud Ninjas Fierce Tige achieves just that perfectly. \u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n\n \u003cdiv class=\"table-section\"\u003e\n\n \u003cdiv class=\"gpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eGPU\u003c\/th\u003e\n \u003cth\u003eVRAM\u003c\/th\u003e\n \u003cth\u003eGPU Clock\u003c\/th\u003e\n \u003cth\u003eMemory Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Max Q Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2280 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 5000 Blackwell\u003c\/td\u003e\n \u003ctd\u003e48GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2377 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 4500 Blackwell\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA 6000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2505 MHz\u003c\/td\u003e\n \u003ctd\u003e2500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5090\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2550 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4500 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e24GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2580 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e20GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2175 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5080\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1875 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2452 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070\u003c\/td\u003e\n \u003ctd\u003e12GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2512 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5060 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2572 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A1000\u003c\/td\u003e\n \u003ctd\u003e8GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1462 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A400\u003c\/td\u003e\n \u003ctd\u003e4GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1762 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \n \u003c\/div\u003e\n \u003c\/div\u003e\n\u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Fierce Tiger","offer_id":42072763334702,"sku":"CN-FIERCE-TIGER-WAEG-5N4S-4G","price":3309.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Define_7_XL-WAEG-5N4S-4G-1.jpg?v=1771955616"},{"product_id":"cloud-ninjas-waru-4n4s-2g-ai-workstation","title":"Steel Wolf AI Workstation","description":"\u003cdiv class=\"subsection cpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"cpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eProcessor Specifications for Cloud Ninjas Steel Wolf\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eCloud Ninjas Steel Wolf focus in offering the best AMD Ryzen series processors together with the best possible GPUs that can fit on this ATX case. Together with four NVMe M.2 and four SATA solid state drives, the Steel Wolf is the perfect solution for a standard form factor system that can run any AI Model, heavy 3D modeling, video editing, and other advanced workstation workloads in a dual-GPU setup. Built for users who need high performance workstation at standard physical sizes. \u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n \u003cdiv class=\"table-section\"\u003e\n \u003cdiv class=\"cpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eCPU\u003c\/th\u003e\n \u003cth\u003eCores \u0026amp; Threads\u003c\/th\u003e\n \u003cth\u003eBase Clock\u003c\/th\u003e\n \u003cth\u003eTurbo Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen 7 9700X\u003c\/td\u003e\n \u003ctd\u003e8C\/16T\u003c\/td\u003e\n \u003ctd\u003e3.80 GHz\u003c\/td\u003e\n \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen 7 9800X3D\u003c\/td\u003e\n \u003ctd\u003e8C\/16T\u003c\/td\u003e\n \u003ctd\u003e4.70 GHz\u003c\/td\u003e\n \u003ctd\u003e5.20 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen 9 9900X\u003c\/td\u003e\n \u003ctd\u003e12C\/24T\u003c\/td\u003e\n \u003ctd\u003e4.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.60 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen 9 9900X3D\u003c\/td\u003e\n \u003ctd\u003e12C\/24T\u003c\/td\u003e\n \u003ctd\u003e4.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen 9 9950X\u003c\/td\u003e\n \u003ctd\u003e16C\/32T\u003c\/td\u003e\n \u003ctd\u003e4.30 GHz\u003c\/td\u003e\n \u003ctd\u003e5.70 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen 9 9950X3D\u003c\/td\u003e\n \u003ctd\u003e16C\/32T\u003c\/td\u003e\n \u003ctd\u003e4.30 GHz\u003c\/td\u003e\n \u003ctd\u003e5.70 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \u003c\/div\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection gpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"gpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas Steel Wolf\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \t\u003cp\u003eIn machine learning, AI, video editing, 3D Modeling among other heavy workloads, the GPU remains the most critical performance component, as modern workloads such as model training, fine-tuning and inference rely on GPU accelaration. A dual-setup high-performance consumer or enterprise GPUs with ample VRAM enables efficient processing of large datasets, execution of complex models, and consistent performance across development and production workflows. High speeds for video editing, and 3D modeling renderization with high VRAM GPUs can be achieved at the highest performance. Fo Ai and machine learning, higher VRAM capacity allows models, data and tensors to remain in the GPU, reducing memory transfers and latency to improve overall performance in matrix and neural network computations.\n\u003c\/p\u003e\n\n\u003cp\u003eFor space-constrained and edge-focused AI infra, carefully selecting a single-GPU configuration can deliver an optimal balance of performance, and system-level optimization. Since chassi size is a constraint in number of possible GPUs, this system values sustainability in performance computation through proper cooling solutions, reliable power delivery, and system-level optimization. In enterprise and preofessional environments, consistent performance over long time periods is essential. And the Cloud Ninjas Steel Wolf achieves just that perfectly. \u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n\n \u003cdiv class=\"table-section\"\u003e\n\n \u003cdiv class=\"gpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eGPU\u003c\/th\u003e\n \u003cth\u003eVRAM\u003c\/th\u003e\n \u003cth\u003eGPU Clock\u003c\/th\u003e\n \u003cth\u003eMemory Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Max Q Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2280 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 5000 Blackwell\u003c\/td\u003e\n \u003ctd\u003e48GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2377 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 4500 Blackwell\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA 6000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2505 MHz\u003c\/td\u003e\n \u003ctd\u003e2500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5090\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2550 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4500 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e24GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2580 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e20GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2175 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5080\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1875 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2452 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070\u003c\/td\u003e\n \u003ctd\u003e12GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2512 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5060 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2572 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A1000\u003c\/td\u003e\n \u003ctd\u003e8GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1462 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A400\u003c\/td\u003e\n \u003ctd\u003e4GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1762 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \n \u003c\/div\u003e\n \u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Steel Wolf Air Cooled","offer_id":42072793677870,"sku":"CN-STEEL-WOLF-WARU-4N4S-2G-AC","price":1939.99,"currency_code":"USD","in_stock":true},{"title":"Cloud Ninjas Steel Wolf Liquid Cooled","offer_id":42109516939310,"sku":"CN-STEEL-WOLF-WARU-4N4S-2G-LQC","price":1929.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Define_7-WARU-4N4S-2G-8.jpg?v=1771954998"},{"product_id":"cloud-ninjas-waru-3n4s-1g-ai-workstation","title":"Loyal Hound AI Workstation","description":"\u003cdiv class=\"subsection cpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"cpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eProcessor Specifications for Cloud Ninjas Loyal Hound\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eCloud Ninjas Loyal Hound focus in offering the best AMD Ryzen series processors together with the best possible GPUs that can fit on this Mini ATX or ITX case. Together with 3 NVMe M.2 and 4 SATA solid state drives, the Loyal Hound is the perfect solution for a small form factor system that runs any AI Model, heavy 3D modeling, video editing, and other advanced workstation workloads. Built for users who need high performance workstation at smaller physical sizes and large storage capacities. \u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n \u003cdiv class=\"table-section\"\u003e\n \u003cdiv class=\"cpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eCPU\u003c\/th\u003e\n \u003cth\u003eCores \u0026amp; Threads\u003c\/th\u003e\n \u003cth\u003eBase Clock\u003c\/th\u003e\n \u003cth\u003eTurbo Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen 7 9700X\u003c\/td\u003e\n \u003ctd\u003e8C\/16T\u003c\/td\u003e\n \u003ctd\u003e3.80 GHz\u003c\/td\u003e\n \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen 7 9800X3D\u003c\/td\u003e\n \u003ctd\u003e8C\/16T\u003c\/td\u003e\n \u003ctd\u003e4.70 GHz\u003c\/td\u003e\n \u003ctd\u003e5.20 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen 9 9900X\u003c\/td\u003e\n \u003ctd\u003e12C\/24T\u003c\/td\u003e\n \u003ctd\u003e4.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.60 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen 9 9900X3D\u003c\/td\u003e\n \u003ctd\u003e12C\/24T\u003c\/td\u003e\n \u003ctd\u003e4.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen 9 9950X\u003c\/td\u003e\n \u003ctd\u003e16C\/32T\u003c\/td\u003e\n \u003ctd\u003e4.30 GHz\u003c\/td\u003e\n \u003ctd\u003e5.70 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAMD Ryzen 9 9950X3D\u003c\/td\u003e\n \u003ctd\u003e16C\/32T\u003c\/td\u003e\n \u003ctd\u003e4.30 GHz\u003c\/td\u003e\n \u003ctd\u003e5.70 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \u003c\/div\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection gpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"gpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas Loyal Hound\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \t\u003cp\u003eIn machine learning, AI, video editing, 3D Modeling among other heavy workloads, the GPU remains the most critical performance component, as modern workloads such as model training, fine-tuning and inference rely on GPU accelaration. A single high-performance consumer or enterprise GPU with ample VRAM enables efficient processing of large datasets, execution of complex models, and consistent performance across development and production workflows. High speeds for video editing, and 3D modeling renderization with high VRAM GPUs can be achieved at the highest performance. Fo Ai and machine learning, higher VRAM capacity allows models, data and tensors to remain in the GPU, reducing memory transfers and latency to improve overall performance in matrix and neural network computations.\n\u003c\/p\u003e\n\n\u003cp\u003eFor space-constrained and edge-focused AI infra, carefully selecting a single-GPU configuration can deliver an optimal balance of performance, and system-level optimization. Since chassi size is a constraint in number of possible GPUs, this system values sustainability in performance computation through proper cooling solutions, reliable power delivery, and system-level optimization. In enterprise and preofessional environments, consistent performance over long time periods is essential. And the Cloud Ninjas Loyal Hound achieves just that perfectly. \u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n\n \u003cdiv class=\"table-section\"\u003e\n\n \u003cdiv class=\"gpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eGPU\u003c\/th\u003e\n \u003cth\u003eVRAM\u003c\/th\u003e\n \u003cth\u003eGPU Clock\u003c\/th\u003e\n \u003cth\u003eMemory Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Max Q Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2280 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 5000 Blackwell\u003c\/td\u003e\n \u003ctd\u003e48GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2377 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 4500 Blackwell\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA 6000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2505 MHz\u003c\/td\u003e\n \u003ctd\u003e2500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5090\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2550 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4500 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e24GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2580 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e20GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2175 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5080\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1875 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2452 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070\u003c\/td\u003e\n \u003ctd\u003e12GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2512 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5060 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2572 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A1000\u003c\/td\u003e\n \u003ctd\u003e8GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1462 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A400\u003c\/td\u003e\n \u003ctd\u003e4GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1762 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \n \u003c\/div\u003e\n \u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Loyal Hound Liquid Cooled","offer_id":42074944864302,"sku":"CN-LOYAL-HOUND-WARU-3N4S-1G-LQC","price":1129.99,"currency_code":"USD","in_stock":true},{"title":"Cloud Ninjas Loyal Hound Air Cooled","offer_id":42074944897070,"sku":"CN-LOYAL-HOUND-WARU-3N4S-1G-AC","price":1179.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Define_7_Mini-WARU-3N4S-1G-1-LQC.jpg?v=1771954987"},{"product_id":"cloud-ninjas-wiuu-5n4s-2g-ai-workstation","title":"Neon Fox AI Workstation","description":"\u003cdiv class=\"subsection cpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"cpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eProcessor Specifications for Cloud Ninjas Neon Fox\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eCloud Ninjas Neon Fox focus in offering the best Intel Ultra series processors together with the best possible GPUs that can fit on this ATX case. Together with five NVMe M.2 and four SATA solid state drives, the Neon Fox is the perfect solution for a standard form factor system that can run any AI Model, heavy 3D modeling, video editing, and other advanced workstation workloads in a dual-GPU setup. Built for users who need high performance workstation at standard physical sizes. \u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n \u003cdiv class=\"table-section\"\u003e\n \u003cdiv class=\"cpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eCPU\u003c\/th\u003e\n \u003cth\u003eCores \u0026amp; Threads\u003c\/th\u003e\n \u003cth\u003eBase Clock\u003c\/th\u003e\n \u003cth\u003eTurbo Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 9 285T\u003c\/td\u003e\n \u003ctd\u003e24C\/24T\u003c\/td\u003e\n \u003ctd\u003e1.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.40 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 9 285K\u003c\/td\u003e\n \u003ctd\u003e24C\/24T\u003c\/td\u003e\n \u003ctd\u003e3.70 GHz\u003c\/td\u003e\n \u003ctd\u003e5.70 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 9 285\u003c\/td\u003e\n \u003ctd\u003e24C\/24T\u003c\/td\u003e\n \u003ctd\u003e2.50 GHz\u003c\/td\u003e\n \u003ctd\u003e5.60 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265T\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e1.50 GHz\u003c\/td\u003e\n \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265KF\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e3.90 GHz\u003c\/td\u003e\n \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265K\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e3.90 GHz\u003c\/td\u003e\n \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265F\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e2.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e2.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 245KF\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e4.20 GHz\u003c\/td\u003e\n \u003ctd\u003e5.20 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 245K\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e4.20 GHz\u003c\/td\u003e\n \u003ctd\u003e5.20 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 245\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e3.50 GHz\u003c\/td\u003e\n \u003ctd\u003e5.10 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 235T\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e2.30 GHz\u003c\/td\u003e\n \u003ctd\u003e5.00 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 235\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e3.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.00 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 225\u003c\/td\u003e\n \u003ctd\u003e10C\/10T\u003c\/td\u003e\n \u003ctd\u003e2.50 GHz\u003c\/td\u003e\n \u003ctd\u003e4.90 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 225F\u003c\/td\u003e\n \u003ctd\u003e10C\/10T\u003c\/td\u003e\n \u003ctd\u003e3.30 GHz\u003c\/td\u003e\n \u003ctd\u003e4.90 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 225\u003c\/td\u003e\n \u003ctd\u003e10C\/10T\u003c\/td\u003e\n \u003ctd\u003e3.30 GHz\u003c\/td\u003e\n \u003ctd\u003e4.90 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \u003c\/div\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection gpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"gpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas Neon Fox\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \t\u003cp\u003eIn machine learning, AI, video editing, 3D Modeling among other heavy workloads, the GPU remains the most critical performance component, as modern workloads such as model training, fine-tuning and inference rely on GPU accelaration. A dual-setup high-performance consumer or enterprise GPUs with ample VRAM enables efficient processing of large datasets, execution of complex models, and consistent performance across development and production workflows. High speeds for video editing, and 3D modeling renderization with high VRAM GPUs can be achieved at the highest performance. Fo Ai and machine learning, higher VRAM capacity allows models, data and tensors to remain in the GPU, reducing memory transfers and latency to improve overall performance in matrix and neural network computations.\n\u003c\/p\u003e\n\n\u003cp\u003eFor space-constrained and edge-focused AI infra, carefully selecting a single-GPU configuration can deliver an optimal balance of performance, and system-level optimization. Since chassi size is a constraint in number of possible GPUs, this system values sustainability in performance computation through proper cooling solutions, reliable power delivery, and system-level optimization. In enterprise and preofessional environments, consistent performance over long time periods is essential. And the Cloud Ninjas Neon Fox achieves just that perfectly. \u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n\n \u003cdiv class=\"table-section\"\u003e\n\n \u003cdiv class=\"gpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eGPU\u003c\/th\u003e\n \u003cth\u003eVRAM\u003c\/th\u003e\n \u003cth\u003eGPU Clock\u003c\/th\u003e\n \u003cth\u003eMemory Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Max Q Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2280 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 5000 Blackwell\u003c\/td\u003e\n \u003ctd\u003e48GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2377 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 4500 Blackwell\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA 6000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2505 MHz\u003c\/td\u003e\n \u003ctd\u003e2500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5090\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2550 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4500 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e24GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2580 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e20GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2175 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5080\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1875 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2452 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070\u003c\/td\u003e\n \u003ctd\u003e12GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2512 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5060 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2572 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A1000\u003c\/td\u003e\n \u003ctd\u003e8GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1462 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A400\u003c\/td\u003e\n \u003ctd\u003e4GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1762 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \n \u003c\/div\u003e\n \u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Neon Fox Air Cooled","offer_id":42077373038638,"sku":"CN-NEON-FOX-WIUU-5N4S-2G-AC","price":1829.99,"currency_code":"USD","in_stock":true},{"title":"Cloud Ninjas Neon Fox Liquid Cooled","offer_id":42109504061486,"sku":"CN-NEON-FOX-WIUU-5N4S-2G-LQC","price":1809.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Define_7-WIUU-5N4S-2G-8.jpg?v=1771955738"},{"product_id":"cloud-ninjas-wiug-2n2s-1g-ai-workstation","title":"Silent Owl AI Workstation","description":"\u003cdiv class=\"subsection cpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"cpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eProcessor Specifications for Cloud Ninjas Silent Owl\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eCloud Ninjas Silent Owl focus in offering the best Intel Core Ultra series processors together with the best possible GPUs that can fit on this Mini ATX or ITX case. Together with two NVMe M.2 and two SATA solid state drives, the Silent Owl is the perfect solution for a small form factor system that runs any AI Model, heavy 3D modeling, video editing, and other advanced workstation workloads. Built for users who need high performance workstation at smaller physical sizes.\u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n \u003cdiv class=\"table-section\"\u003e\n \u003cdiv class=\"cpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eCPU\u003c\/th\u003e\n \u003cth\u003eCores \u0026amp; Threads\u003c\/th\u003e\n \u003cth\u003eBase Clock\u003c\/th\u003e\n \u003cth\u003eTurbo Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 9 285T\u003c\/td\u003e\n \u003ctd\u003e24C\/24T\u003c\/td\u003e\n \u003ctd\u003e1.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.40 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 9 285K\u003c\/td\u003e\n \u003ctd\u003e24C\/24T\u003c\/td\u003e\n \u003ctd\u003e3.70 GHz\u003c\/td\u003e\n \u003ctd\u003e5.70 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 9 285\u003c\/td\u003e\n \u003ctd\u003e24C\/24T\u003c\/td\u003e\n \u003ctd\u003e2.50 GHz\u003c\/td\u003e\n \u003ctd\u003e5.60 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265T\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e1.50 GHz\u003c\/td\u003e\n \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265KF\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e3.90 GHz\u003c\/td\u003e\n \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265K\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e3.90 GHz\u003c\/td\u003e\n \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265F\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e2.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e2.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 245KF\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e4.20 GHz\u003c\/td\u003e\n \u003ctd\u003e5.20 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 245K\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e4.20 GHz\u003c\/td\u003e\n \u003ctd\u003e5.20 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 245\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e3.50 GHz\u003c\/td\u003e\n \u003ctd\u003e5.10 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 235T\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e2.30 GHz\u003c\/td\u003e\n \u003ctd\u003e5.00 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 235\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e3.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.00 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 225\u003c\/td\u003e\n \u003ctd\u003e10C\/10T\u003c\/td\u003e\n \u003ctd\u003e2.50 GHz\u003c\/td\u003e\n \u003ctd\u003e4.90 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 225F\u003c\/td\u003e\n \u003ctd\u003e10C\/10T\u003c\/td\u003e\n \u003ctd\u003e3.30 GHz\u003c\/td\u003e\n \u003ctd\u003e4.90 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 225\u003c\/td\u003e\n \u003ctd\u003e10C\/10T\u003c\/td\u003e\n \u003ctd\u003e3.30 GHz\u003c\/td\u003e\n \u003ctd\u003e4.90 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \u003c\/div\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection gpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"gpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas Silent Owl\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \t\u003cp\u003eIn machine learning, AI, video editing, 3D Modeling among other heavy workloads, the GPU remains the most critical performance component, as modern workloads such as model training, fine-tuning and inference rely on GPU accelaration. A single high-performance consumer or enterprise GPU with ample VRAM enables efficient processing of large datasets, execution of complex models, and consistent performance across development and production workflows. High speeds for video editing, and 3D modeling renderization with high VRAM GPUs can be achieved at the highest performance. Fo Ai and machine learning, higher VRAM capacity allows models, data and tensors to remain in the GPU, reducing memory transfers and latency to improve overall performance in matrix and neural network computations.\n\u003c\/p\u003e\n\n\u003cp\u003eFor space-constrained and edge-focused AI infra, carefully selecting a single-GPU configuration can deliver an optimal balance of performance, and system-level optimization. Since chassi size is a constraint in number of possible GPUs, this system values sustainability in performance computation through proper cooling solutions, reliable power delivery, and system-level optimization. In enterprise and preofessional environments, consistent performance over long time periods is essential. And the Cloud Ninjas Silent Owl achieves just that perfectly. \u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n\n \u003cdiv class=\"table-section\"\u003e\n\n \u003cdiv class=\"gpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eGPU\u003c\/th\u003e\n \u003cth\u003eVRAM\u003c\/th\u003e\n \u003cth\u003eGPU Clock\u003c\/th\u003e\n \u003cth\u003eMemory Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Max Q Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2280 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 5000 Blackwell\u003c\/td\u003e\n \u003ctd\u003e48GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2377 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 4500 Blackwell\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA 6000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2505 MHz\u003c\/td\u003e\n \u003ctd\u003e2500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5090\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2550 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4500 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e24GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2580 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e20GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2175 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5080\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1875 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2452 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070\u003c\/td\u003e\n \u003ctd\u003e12GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2512 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5060 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2572 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A1000\u003c\/td\u003e\n \u003ctd\u003e8GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1462 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A400\u003c\/td\u003e\n \u003ctd\u003e4GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1762 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \n \u003c\/div\u003e\n \u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Silent Owl Liquid Cooled","offer_id":42077423861806,"sku":"CN-SILENT-OWL-WIUG-2N2S-1G-LQC","price":1129.99,"currency_code":"USD","in_stock":true},{"title":"Cloud Ninjas Silent Owl Air Cooled","offer_id":42077423894574,"sku":"CN-SILENT-OWL-WIUG-2N2S-1G-AC","price":1169.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Define_7_Mini-WIUG-2N2S-1G-1-LQC.jpg?v=1771954954"},{"product_id":"cloud-ninjas-wiuu-3n4s-1g-ai-workstation","title":"Ghost Viper AI Workstation","description":"\u003cdiv class=\"subsection cpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"cpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eProcessor Specifications for Cloud Ninjas Ghost Viper\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eCloud Ninjas Ghost Viper focus in offering the best Intel Core Ultra series processors together with the best possible GPUs that can fit on this Mini ATX or ITX case. Together with three NVMe M.2 and four SATA solid state drives, the Ghost Viper is the perfect solution for a small form factor system that runs any AI Model, heavy 3D modeling, video editing, and other advanced workstation workloads. Built for users who need high performance workstation at smaller chassis sizes.\u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n \u003cdiv class=\"table-section\"\u003e\n \u003cdiv class=\"cpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eCPU\u003c\/th\u003e\n \u003cth\u003eCores \u0026amp; Threads\u003c\/th\u003e\n \u003cth\u003eBase Clock\u003c\/th\u003e\n \u003cth\u003eTurbo Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 9 285T\u003c\/td\u003e\n \u003ctd\u003e24C\/24T\u003c\/td\u003e\n \u003ctd\u003e1.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.40 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 9 285K\u003c\/td\u003e\n \u003ctd\u003e24C\/24T\u003c\/td\u003e\n \u003ctd\u003e3.70 GHz\u003c\/td\u003e\n \u003ctd\u003e5.70 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 9 285\u003c\/td\u003e\n \u003ctd\u003e24C\/24T\u003c\/td\u003e\n \u003ctd\u003e2.50 GHz\u003c\/td\u003e\n \u003ctd\u003e5.60 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265T\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e1.50 GHz\u003c\/td\u003e\n \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265KF\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e3.90 GHz\u003c\/td\u003e\n \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265K\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e3.90 GHz\u003c\/td\u003e\n \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265F\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e2.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e2.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 245KF\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e4.20 GHz\u003c\/td\u003e\n \u003ctd\u003e5.20 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 245K\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e4.20 GHz\u003c\/td\u003e\n \u003ctd\u003e5.20 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 245\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e3.50 GHz\u003c\/td\u003e\n \u003ctd\u003e5.10 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 235T\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e2.30 GHz\u003c\/td\u003e\n \u003ctd\u003e5.00 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 235\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e3.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.00 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 225\u003c\/td\u003e\n \u003ctd\u003e10C\/10T\u003c\/td\u003e\n \u003ctd\u003e2.50 GHz\u003c\/td\u003e\n \u003ctd\u003e4.90 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 225F\u003c\/td\u003e\n \u003ctd\u003e10C\/10T\u003c\/td\u003e\n \u003ctd\u003e3.30 GHz\u003c\/td\u003e\n \u003ctd\u003e4.90 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 225\u003c\/td\u003e\n \u003ctd\u003e10C\/10T\u003c\/td\u003e\n \u003ctd\u003e3.30 GHz\u003c\/td\u003e\n \u003ctd\u003e4.90 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \u003c\/div\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection gpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"gpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas Ghost Viper\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \t\u003cp\u003eIn machine learning, AI, video editing, 3D Modeling among other heavy workloads, the GPU is without a doubt the most critical performance component, as modern workloads such as model training, fine-tuning and inference rely on GPU accelaration. A single high-performance consumer or enterprise GPU with ample VRAM enables efficient processing of large datasets, execution of complex models, and consistent performance across development and production workflows. High speeds for video editing, and 3D modeling renderization with high VRAM GPUs can be achieved at the highest performance. Fo Ai and machine learning, higher VRAM capacity allows models, data and tensors to remain in the GPU, reducing memory transfers and latency to improve overall performance in matrix and neural network computations.\n\u003c\/p\u003e\n\n\u003cp\u003eFor space-constrained and edge-focused AI infra, carefully selecting a single-GPU can deliver an optimal balance of performance, and system-level optimization. Since chassis size is a constraint in number of possible GPUs, this system values sustainability in performance computation through proper cooling solutions, reliable power delivery, and system-level optimization. In enterprise and preofessional environments, consistent performance over long time periods is essential. And the Cloud Ninjas Ghost Viper achieves just that perfectly. \u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n\n \u003cdiv class=\"table-section\"\u003e\n\n \u003cdiv class=\"gpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eGPU\u003c\/th\u003e\n \u003cth\u003eVRAM\u003c\/th\u003e\n \u003cth\u003eGPU Clock\u003c\/th\u003e\n \u003cth\u003eMemory Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Max Q Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2280 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 5000 Blackwell\u003c\/td\u003e\n \u003ctd\u003e48GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2377 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 4500 Blackwell\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA 6000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2505 MHz\u003c\/td\u003e\n \u003ctd\u003e2500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5090\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2550 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4500 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e24GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2580 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e20GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2175 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5080\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1875 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2452 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070\u003c\/td\u003e\n \u003ctd\u003e12GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2512 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5060 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2572 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A1000\u003c\/td\u003e\n \u003ctd\u003e8GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1462 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A400\u003c\/td\u003e\n \u003ctd\u003e4GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1762 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \n \u003c\/div\u003e\n \u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Ghost Viper Liquid Cooled","offer_id":42082094481454,"sku":"CN-GHOST-VIPER-WIUU-3N4S-1G-LQC","price":1109.99,"currency_code":"USD","in_stock":true},{"title":"Cloud Ninjas Ghost Viper Air Cooled","offer_id":42082094514222,"sku":"CN-GHOST-VIPER-WIUU-3N4S-1G-AC","price":1159.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Define_7_Mini-WIUU-3N4S-1G-1-LQC.jpg?v=1771954936"},{"product_id":"cloud-ninjas-wiug-3n4s-1g-a-ai-workstation","title":"Arctic Falcon AI Workstation","description":"\u003cdiv class=\"subsection cpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"cpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eProcessor Specifications for Cloud Ninjas Arctic Falcon\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eCloud Ninjas Arctic Falcon focus in offering the best Intel Core Ultra series processors together with the best possible GPUs that can fit on this Mini ATX or ITX case. Together with three NVMe M.2 and four SATA solid state drives and Intel chipset B860, the Arctic Falcon is the perfect solution for a small form factor system that runs any AI Model, heavy 3D modeling, video editing, and other advanced workstation workloads. Built for users who need high performance workstation at smaller chassis sizes.\u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n \u003cdiv class=\"table-section\"\u003e\n \u003cdiv class=\"cpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eCPU\u003c\/th\u003e\n \u003cth\u003eCores \u0026amp; Threads\u003c\/th\u003e\n \u003cth\u003eBase Clock\u003c\/th\u003e\n \u003cth\u003eTurbo Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 9 285T\u003c\/td\u003e\n \u003ctd\u003e24C\/24T\u003c\/td\u003e\n \u003ctd\u003e1.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.40 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 9 285K\u003c\/td\u003e\n \u003ctd\u003e24C\/24T\u003c\/td\u003e\n \u003ctd\u003e3.70 GHz\u003c\/td\u003e\n \u003ctd\u003e5.70 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 9 285\u003c\/td\u003e\n \u003ctd\u003e24C\/24T\u003c\/td\u003e\n \u003ctd\u003e2.50 GHz\u003c\/td\u003e\n \u003ctd\u003e5.60 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265T\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e1.50 GHz\u003c\/td\u003e\n \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265KF\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e3.90 GHz\u003c\/td\u003e\n \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265K\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e3.90 GHz\u003c\/td\u003e\n \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265F\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e2.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e2.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 245KF\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e4.20 GHz\u003c\/td\u003e\n \u003ctd\u003e5.20 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 245K\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e4.20 GHz\u003c\/td\u003e\n \u003ctd\u003e5.20 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 245\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e3.50 GHz\u003c\/td\u003e\n \u003ctd\u003e5.10 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 235T\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e2.30 GHz\u003c\/td\u003e\n \u003ctd\u003e5.00 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 235\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e3.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.00 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 225\u003c\/td\u003e\n \u003ctd\u003e10C\/10T\u003c\/td\u003e\n \u003ctd\u003e2.50 GHz\u003c\/td\u003e\n \u003ctd\u003e4.90 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 225F\u003c\/td\u003e\n \u003ctd\u003e10C\/10T\u003c\/td\u003e\n \u003ctd\u003e3.30 GHz\u003c\/td\u003e\n \u003ctd\u003e4.90 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 225\u003c\/td\u003e\n \u003ctd\u003e10C\/10T\u003c\/td\u003e\n \u003ctd\u003e3.30 GHz\u003c\/td\u003e\n \u003ctd\u003e4.90 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \u003c\/div\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection gpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"gpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas Arctic Falcon\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \t\u003cp\u003eIn machine learning, AI, video editing, 3D Modeling among other heavy workloads, the GPU is without a doubt the most critical performance component, as modern workloads such as model training, fine-tuning and inference rely on GPU accelaration. A single high-performance consumer or enterprise GPU with ample VRAM enables efficient processing of large datasets, execution of complex models, and consistent performance across development and production workflows. High speeds for video editing, and 3D modeling renderization with high VRAM GPUs can be achieved at the highest performance. Fo Ai and machine learning, higher VRAM capacity allows models, data and tensors to remain in the GPU, reducing memory transfers and latency to improve overall performance in matrix and neural network computations.\n\u003c\/p\u003e\n\n\u003cp\u003eFor space-constrained and edge-focused AI infra, carefully selecting a single-GPU can deliver an optimal balance of performance, and system-level optimization. Since chassis size is a constraint in number of possible GPUs, this system values sustainability in performance computation through proper cooling solutions, reliable power delivery, and system-level optimization. In enterprise and preofessional environments, consistent performance over long time periods is essential. And the Cloud Ninjas Arctic Falcon achieves just that perfectly. \u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n\n \u003cdiv class=\"table-section\"\u003e\n\n \u003cdiv class=\"gpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eGPU\u003c\/th\u003e\n \u003cth\u003eVRAM\u003c\/th\u003e\n \u003cth\u003eGPU Clock\u003c\/th\u003e\n \u003cth\u003eMemory Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Max Q Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2280 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 5000 Blackwell\u003c\/td\u003e\n \u003ctd\u003e48GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2377 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 4500 Blackwell\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA 6000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2505 MHz\u003c\/td\u003e\n \u003ctd\u003e2500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5090\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2550 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4500 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e24GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2580 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e20GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2175 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5080\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1875 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2452 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070\u003c\/td\u003e\n \u003ctd\u003e12GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2512 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5060 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2572 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A1000\u003c\/td\u003e\n \u003ctd\u003e8GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1462 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A400\u003c\/td\u003e\n \u003ctd\u003e4GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1762 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \n \u003c\/div\u003e\n \u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Arctic Falcon Liquid Cooled","offer_id":42082165653550,"sku":"CN-ARCTIC-FALCON-WIUG-3N4S-1G-A-LQC","price":1119.99,"currency_code":"USD","in_stock":true},{"title":"Cloud Ninjas Arctic Falcon Air Cooled","offer_id":42082165686318,"sku":"CN-ARCTIC-FALCON-WIUG-3N4S-1G-A-AC","price":1169.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Define_7_Mini-WIUG-3N4S-1G-A-1-LQC.jpg?v=1771955754"},{"product_id":"cloud-ninjas-wiug-3n4s-1g-b-ai-workstation","title":"Bold Eagle AI Workstation","description":"\u003cdiv class=\"subsection cpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"cpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eProcessor Specifications for Cloud Ninjas Bold Eagle\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eCloud Ninjas Bold Eagle focus in offering the best Intel Core Ultra series processors together with the best possible GPUs that can fit on this Mini ATX or ITX case. Together with three NVMe M.2 and four SATA solid state drives and Intel chipset Z890, the Bold Eagle is the perfect solution for a small form factor system that runs any AI Model, heavy 3D modeling, video editing, and other advanced workstation workloads. Built for users who need high performance workstation at smaller chassis sizes.\u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n \u003cdiv class=\"table-section\"\u003e\n \u003cdiv class=\"cpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eCPU\u003c\/th\u003e\n \u003cth\u003eCores \u0026amp; Threads\u003c\/th\u003e\n \u003cth\u003eBase Clock\u003c\/th\u003e\n \u003cth\u003eTurbo Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 9 285T\u003c\/td\u003e\n \u003ctd\u003e24C\/24T\u003c\/td\u003e\n \u003ctd\u003e1.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.40 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 9 285K\u003c\/td\u003e\n \u003ctd\u003e24C\/24T\u003c\/td\u003e\n \u003ctd\u003e3.70 GHz\u003c\/td\u003e\n \u003ctd\u003e5.70 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 9 285\u003c\/td\u003e\n \u003ctd\u003e24C\/24T\u003c\/td\u003e\n \u003ctd\u003e2.50 GHz\u003c\/td\u003e\n \u003ctd\u003e5.60 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265T\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e1.50 GHz\u003c\/td\u003e\n \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265KF\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e3.90 GHz\u003c\/td\u003e\n \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265K\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e3.90 GHz\u003c\/td\u003e\n \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265F\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e2.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 7 265\u003c\/td\u003e\n \u003ctd\u003e20C\/20T\u003c\/td\u003e\n \u003ctd\u003e2.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 245KF\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e4.20 GHz\u003c\/td\u003e\n \u003ctd\u003e5.20 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 245K\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e4.20 GHz\u003c\/td\u003e\n \u003ctd\u003e5.20 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 245\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e3.50 GHz\u003c\/td\u003e\n \u003ctd\u003e5.10 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 235T\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e2.30 GHz\u003c\/td\u003e\n \u003ctd\u003e5.00 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 235\u003c\/td\u003e\n \u003ctd\u003e14C\/14T\u003c\/td\u003e\n \u003ctd\u003e3.40 GHz\u003c\/td\u003e\n \u003ctd\u003e5.00 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 225\u003c\/td\u003e\n \u003ctd\u003e10C\/10T\u003c\/td\u003e\n \u003ctd\u003e2.50 GHz\u003c\/td\u003e\n \u003ctd\u003e4.90 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 225F\u003c\/td\u003e\n \u003ctd\u003e10C\/10T\u003c\/td\u003e\n \u003ctd\u003e3.30 GHz\u003c\/td\u003e\n \u003ctd\u003e4.90 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Ultra 5 225\u003c\/td\u003e\n \u003ctd\u003e10C\/10T\u003c\/td\u003e\n \u003ctd\u003e3.30 GHz\u003c\/td\u003e\n \u003ctd\u003e4.90 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \u003c\/div\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection gpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"gpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas Bold Eagle\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \t\u003cp\u003eIn machine learning, AI, video editing, 3D Modeling among other heavy workloads, the GPU is without a doubt the most critical performance component, as modern workloads such as model training, fine-tuning and inference rely on GPU accelaration. A single high-performance consumer or enterprise GPU with ample VRAM enables efficient processing of large datasets, execution of complex models, and consistent performance across development and production workflows. High speeds for video editing, and 3D modeling renderization with high VRAM GPUs can be achieved at the highest performance. Fo Ai and machine learning, higher VRAM capacity allows models, data and tensors to remain in the GPU, reducing memory transfers and latency to improve overall performance in matrix and neural network computations.\n\u003c\/p\u003e\n\n\u003cp\u003eFor space-constrained and edge-focused AI infra, carefully selecting a single-GPU can deliver an optimal balance of performance, and system-level optimization. Since chassis size is a constraint in number of possible GPUs, this system values sustainability in performance computation through proper cooling solutions, reliable power delivery, and system-level optimization. In enterprise and preofessional environments, consistent performance over long time periods is essential. And the Cloud Ninjas Bold Eagle achieves just that perfectly. \u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n\n \u003cdiv class=\"table-section\"\u003e\n\n \u003cdiv class=\"gpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eGPU\u003c\/th\u003e\n \u003cth\u003eVRAM\u003c\/th\u003e\n \u003cth\u003eGPU Clock\u003c\/th\u003e\n \u003cth\u003eMemory Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Max Q Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2280 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 5000 Blackwell\u003c\/td\u003e\n \u003ctd\u003e48GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2377 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 4500 Blackwell\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA 6000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2505 MHz\u003c\/td\u003e\n \u003ctd\u003e2500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5090\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2550 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4500 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e24GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2580 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e20GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2175 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5080\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1875 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2452 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070\u003c\/td\u003e\n \u003ctd\u003e12GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2512 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5060 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2572 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A1000\u003c\/td\u003e\n \u003ctd\u003e8GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1462 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A400\u003c\/td\u003e\n \u003ctd\u003e4GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1762 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \n \u003c\/div\u003e\n \u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Bold Eagle Liquid Cooled","offer_id":42082623750190,"sku":"CN-BOLD-EAGLE-WIUG-3N4S-1G-B-LQC","price":1129.99,"currency_code":"USD","in_stock":true},{"title":"Cloud Ninjas Bold Eagle Air Cooled","offer_id":42082623782958,"sku":"CN-BOLD-EAGLE-WIUG-3N4S-1G-B-AC","price":1179.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Define_7_Mini-WIUG-3N4S-1G-B-1-LQC.jpg?v=1771954862"}],"url":"https:\/\/cloudninjas.com\/collections\/cloud-ninjas-workstations-for-ai-gpu-heavy-softwares.oembed","provider":"Cloud Ninjas","version":"1.0","type":"link"}