{"product_id":"cloud-ninjas-watru-4n4s-4g-ai-workstation","title":"Cloud Ninjas WATRU-4N4S-4G 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 WATRU-4N4S-4G\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eCloud Ninjas WATRU-4N4S-4G 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 WATRU-4N4S-4G\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 WATRU-4N4S-4G","offer_id":42056864759854,"sku":"CN-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","url":"https:\/\/cloudninjas.com\/products\/cloud-ninjas-watru-4n4s-4g-ai-workstation","provider":"Cloud Ninjas","version":"1.0","type":"link"}