{"title":"Cloud Ninjas Workstations for High Powered Computing","description":"\u003cdiv class=\"seo-sections\"\u003e\n \u003csection class=\"seo-section fade-in\"\u003e\n \u003cdiv class=\"seo-content\"\u003e\n \u003ch2\u003eHigh-Performance Workstations for Compact Size Computing\u003c\/h2\u003e\n \u003cp\u003e\nOptimize performance in space-constrained environments with workstations built for Compact Size applications, Data Science, NVIDIA Studio, and Scientific Computing. These systems deliver full-scale computing power while supporting specialized workloads, ensuring professionals can run advanced simulations, AI tasks, and GPU-intensive projects even in smaller setups.\n \u003c\/p\u003e\n \u003c\/div\u003e\n \u003cdiv class=\"seo-image\"\u003e\n \u003cimg src=\"https:\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/fractal_min.jpg?v=1774379766\" alt=\"Compact workstation rendering complex simulations or AI models\" width=\"700px\" height=\"auto\" loading=\"lazy\"\u003e\n \u003c\/div\u003e\n \u003c\/section\u003e\n\n \u003csection class=\"seo-section fade-in\"\u003e\n \u003cdiv class=\"seo-content\"\u003e\n \u003ch2\u003eFuture-Proof Your Data Science and AI Workflows\u003c\/h2\u003e\n \u003cp\u003e\n Our workstations handle data-heavy applications, AI training, and scientific computing with ease. Built to support NVIDIA Studio and advanced GPU workloads, these systems provide high memory capacity, multi-core CPUs, and fast storage, giving data scientists and engineers the performance and reliability they need for complex projects.\n \u003c\/p\u003e\n \u003c\/div\u003e\n \u003cdiv class=\"seo-image\"\u003e\n \u003cimg src=\"https:\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Data_scientist_working_with_large_datasets_and_multiple_monitors.webp?v=1774379954\" alt=\"Data scientist working with large datasets and multiple monitors\" width=\"600px\" height=\"auto\" loading=\"lazy\"\u003e\n \u003c\/div\u003e\n \u003c\/section\u003e\n\n \u003csection class=\"seo-section fade-in\"\u003e\n \u003cdiv class=\"seo-content\"\u003e\n \u003ch2\u003eSeamless Compatibility Across High-Powered Applications\u003c\/h2\u003e\n \u003cp\u003e\nFrom Compact Size tasks to Scientific Computing, Data Science, and NVIDIA Studio applications, these workstations are optimized for efficient multi-threaded processing, GPU acceleration, and memory-intensive workloads. Professionals can focus on research, AI development, and analytics without hardware limitations\u003c\/p\u003e\n \u003c\/div\u003e\n \u003cdiv class=\"seo-image\"\u003e\n \u003cimg src=\"https:\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Engineer_or_researcher_interacting_with_simulation_software..jpg?v=1774381789\" width=\"600px\" height=\"auto\" loading=\"lazy\"\u003e\n \u003c\/div\u003e\n \u003c\/section\u003e\n\n \u003csection class=\"seo-section fade-in\"\u003e\n \u003cdiv class=\"seo-content\"\u003e\n \u003ch2\u003eWorkstations Built for Professional Computational Tasks\u003c\/h2\u003e\n \u003cp\u003e\nMaximize efficiency with systems designed for high-core CPUs, professional GPUs, and ample memory bandwidth, supporting scientific modeling, AI processing, and NVIDIA Studio workloads. These workstations deliver fast, reliable performance for professionals who require accuracy, speed, and stability in demanding computational tasks.\n \u003c\/p\u003e\n \u003c\/div\u003e\n \u003cdiv class=\"seo-image\"\u003e\n \u003cimg src=\"https:\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/programmer-s-workspace-setup-stockcake.webp?v=1774381689\" alt=\"Research lab or creative studio desk with workstation actively running simulations\" height=\"auto\" width=\"600px\" loading=\"lazy\"\u003e\n \u003c\/div\u003e\n \u003c\/section\u003e\n\u003c\/div\u003e\n","products":[{"product_id":"cloud-ninjas-workstations-for-data-science-intel-xeon-edition","title":"Cloud Ninjas Workstations for Data Science Intel Xeon Edition","description":"\u003cdiv class=\"subsection cpu-subsection\"\u003e\n\u003cdiv id=\"cpu-subsection-toggle\" class=\"sub-section-head\"\u003e\u003cspan class=\"sub-section-tl\"\u003eProcessor Specifications for Cloud Ninjas Data Science Intel Xeon Edition Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eThe CPU is a critical component in a data science workstation, as many analytical and data manipulation tasks rely heavily on CPU performance. Strong multi-core capability improves parallel data processing in programs such as MATLAB or Python. High clock speeds enhance responsiveness during interactive analysis and visualization; Single-threaded applications such as R also benefit. The selection of CPU depends on which workflow.\u003c\/p\u003e\n\u003c\/div\u003e\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\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"subsection gpu-subsection\"\u003e\n\u003cdiv id=\"gpu-subsection-toggle\" class=\"sub-section-head\"\u003e\u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas Data Science Intel Xeon Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eThe GPU enhances data science workflows by accelerating machine learning models, simulations, and advanced visualizations. A modern GPU with sufficient memory improves performance when working with large datasets and complex models, making GPU capability an important factor for scalable data science and analytics workloads.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"table-section\"\u003e\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 RTX PRO 4000 Blackwell\u003c\/td\u003e\n \u003ctd\u003e24GB GGDR7\u003c\/td\u003e\n \u003ctd\u003e1590 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 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 \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\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Primal Gorilla","offer_id":42074987200558,"sku":"Cloud Ninjas WIW35U-5N6S-4G-Intel-Xeon-Edition-Data Science","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-workstations-for-scientific-computing-intel-xeon-edition","title":"Cloud Ninjas Workstations for Scientific Computing Intel Xeon Edition","description":"\u003cdiv class=\"subsection cpu-subsection\"\u003e\n\u003cdiv id=\"cpu-subsection-toggle\" class=\"sub-section-head\"\u003e\u003cspan class=\"sub-section-tl\"\u003eProcessor Specifications for Cloud Ninjas Scientific Computing Intel Xeon Edition Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eThe CPU is a core component in scientific computing workloads, as many simulations and numerical methods rely heavily on multi-core and multi-threaded CPU performance. High core counts improve parallel computation efficiency, while strong per-core performance enhances single-threaded calculations and solver responsiveness.\u003c\/p\u003e\n\u003c\/div\u003e\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\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"subsection gpu-subsection\"\u003e\n\u003cdiv id=\"gpu-subsection-toggle\" class=\"sub-section-head\"\u003e\u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas Scientific Computing Intel Xeon Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eThe GPU accelerates many scientific computing workloads, including matrix operations, simulations, and data visualization. A modern GPU with strong compute capability and sufficient memory improves performance for GPU-accelerated frameworks, making GPU selection an important factor for scalable scientific computing and research workloads\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"table-section\"\u003e\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 RTX PRO 4000 Blackwell\u003c\/td\u003e\n \u003ctd\u003e24GB GGDR7\u003c\/td\u003e\n \u003ctd\u003e1590 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 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\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\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Primal Gorilla","offer_id":42075036254254,"sku":"Cloud Ninjas WIW35U-5N6S-4G-Intel-Xeon-Edition-Scientific Computing","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-workstations-for-compact-size-amd-ryzen-edition","title":"Cloud Ninjas Workstations for Compact Size AMD Ryzen Edition","description":"\u003cdiv class=\"subsection cpu-subsection\"\u003e\n\u003cdiv id=\"cpu-subsection-toggle\" class=\"sub-section-head\"\u003e\u003cspan class=\"sub-section-tl\"\u003eProcessor Specifications for Cloud Ninjas' AMD Ryzen Edition Compact Size Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eIn a compact size workstation, the CPU must deliver strong performance while operating efficiently within thermal constraints. High clock speeds improve responsiveness for interactive tasks, while balanced core counts support multitasking without excessive heat output, making thermal efficiency a key factor in CPU selection.\u003c\/p\u003e\n\u003c\/div\u003e\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\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"subsection gpu-subsection\"\u003e\n\u003cdiv id=\"gpu-subsection-toggle\" class=\"sub-section-head\"\u003e\u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas' AMD Ryzen Edition Compact Size Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eThe GPU in a compact size workstation must balance performance, power consumption, and thermal output. Efficient GPUs with sufficient memory enable accelerated workflows and real-time rendering while maintaining stable operation in limited space. Proper airflow and cooling design are critical to ensure consistent GPU performance in compact systems.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"table-section\"\u003e\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 RTX PRO 4000 Blackwell\u003c\/td\u003e\n \u003ctd\u003e24GB GGDR7\u003c\/td\u003e\n \u003ctd\u003e1590 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 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 \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\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Phantom Lynx Liquid Cooled","offer_id":42082237087790,"sku":"Cloud Ninjas WARU-2N2S-1G-LC-AMD-Ryzen-Edition-Compact Size","price":1249.99,"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-workstations-for-data-science-amd-ryzen-edition","title":"Cloud Ninjas Workstations for Data Science AMD Ryzen Edition","description":"\u003cdiv class=\"subsection cpu-subsection\"\u003e\n\u003cdiv id=\"cpu-subsection-toggle\" class=\"sub-section-head\"\u003e\u003cspan class=\"sub-section-tl\"\u003eProcessor Specifications for Cloud Ninjas' AMD Ryzen Edition Data Science Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eThe CPU is the central component in a data science workstation. High core and thread counts significantly improve performance in data preprocessing, statistical analysis, and many machine learning workflows. Strong single-core speed also benefits interactive tasks in notebooks and development environments, making modern AMD Ryzen processors well-suited for balanced data science workloads.\u003c\/p\u003e\n\u003c\/div\u003e\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\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"subsection gpu-subsection\"\u003e\n\u003cdiv id=\"gpu-subsection-toggle\" class=\"sub-section-head\"\u003e\u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas' AMD Ryzen Edition Data Science Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eThe GPU plays an important but workload-dependent role in a data science workstation. For traditional analytics and many classical machine learning tasks, GPU impact is limited. However, for deep learning, large-scale model training, and accelerated libraries, a capable GPU with sufficient VRAM can dramatically reduce training times and improve experimentation throughput.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"table-section\"\u003e\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 RTX PRO 4000 Blackwell\u003c\/td\u003e\n \u003ctd\u003e24GB GGDR7\u003c\/td\u003e\n \u003ctd\u003e1590 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 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 \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\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Mythical Dragon","offer_id":42089025896494,"sku":"Cloud Ninjas WATRU-6N4S-4G-GPU-AMD-Ryzen-Edition-Data Science","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-workstations-for-scientific-computing-amd-ryzen-edition","title":"Cloud Ninjas Workstations for Scientific Computing AMD Ryzen Edition","description":"\u003cdiv class=\"subsection cpu-subsection\"\u003e\n\u003cdiv id=\"cpu-subsection-toggle\" class=\"sub-section-head\"\u003e\u003cspan class=\"sub-section-tl\"\u003eProcessor Specifications for Cloud Ninjas' AMD Ryzen Edition Scientific Computing Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eThe CPU is the most critical component in a scientific computing workstation. High core and thread counts dramatically accelerate parallel solvers, simulations, and numerical analysis tasks. Processors such as AMD Threadripper excel in workloads that scale across many cores, while strong memory bandwidth and I\/O capacity help maintain efficient data flow in demanding compute environments.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"table-section\"\u003e\n\u003cdiv class=\"cpu-compatibility-table\"\u003e\n\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\u003cdiv class=\"subsection gpu-subsection\"\u003e\n\u003cdiv id=\"gpu-subsection-toggle\" class=\"sub-section-head\"\u003e\u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas' AMD Ryzen Edition Scientific Computing Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eThe GPU provides workload-specific acceleration in a scientific computing workstation. For applications that support CUDA, OpenCL, or other GPU compute frameworks, a capable GPU with sufficient VRAM can significantly reduce computation times. However, many traditional scientific codes remain CPU-bound, making GPU selection dependent on the specific software stack and research requirements.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"table-section\"\u003e\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 RTX PRO 4000 Blackwell\u003c\/td\u003e\n \u003ctd\u003e24GB GGDR7\u003c\/td\u003e\n \u003ctd\u003e1590 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 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 \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\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Mythical Dragon","offer_id":42092461195310,"sku":"Cloud Ninjas WATRU-6N4S-4G-GPU-AMD-Ryzen-Edition-Scientific Computing","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-workstations-for-scientific-computing-amd-epyc-edition","title":"Cloud Ninjas Workstations for Scientific Computing AMD Epyc Edition","description":"\u003cdiv class=\"subsection cpu-subsection\"\u003e\n\u003cdiv id=\"cpu-subsection-toggle\" class=\"sub-section-head\"\u003e\u003cspan class=\"sub-section-tl\"\u003eProcessor Specifications for Cloud Ninjas' AMD Epyc Edition Scientific Computing Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eThe CPU is the most critical component in an AMD EPYC scientific computing workstation. Extremely high core and thread counts dramatically accelerate parallel solvers, finite element analysis, and numerical simulations. EPYC processors also provide strong memory bandwidth and PCIe lane availability, which are essential for data-heavy scientific workloads and multi-device configurations.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"table-section\"\u003e\n\u003cdiv class=\"cpu-compatibility-table\"\u003e\n\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 \u003c\/div\u003e\n\u003cdiv class=\"subsection gpu-subsection\"\u003e\n\u003cdiv id=\"gpu-subsection-toggle\" class=\"sub-section-head\"\u003e\u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas' AMD Epyc Edition Scientific Computing Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eThe GPU provides workload-specific acceleration in a scientific computing workstation. For applications that support CUDA, OpenCL, or other GPU compute frameworks, a capable GPU with sufficient VRAM can significantly reduce computation times. However, many traditional scientific codes remain CPU-bound, making GPU selection dependent on the specific software stack and research requirements.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"table-section\"\u003e\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 RTX PRO 4000 Blackwell\u003c\/td\u003e\n \u003ctd\u003e24GB GGDR7\u003c\/td\u003e\n \u003ctd\u003e1590 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 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 \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\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Rhino Juggernaut","offer_id":42105471926318,"sku":"Cloud Ninjas WAEG-5N6S-4G-GPU-AMD-Epyc-Edition-Scientific Computing","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-workstations-for-compact-size-intel-core-ultra-edition","title":"Cloud Ninjas Workstations for Compact Size Intel Core Ultra Edition","description":"\u003cdiv class=\"subsection cpu-subsection\"\u003e\n\u003cdiv id=\"cpu-subsection-toggle\" class=\"sub-section-head\"\u003e\u003cspan class=\"sub-section-tl\"\u003eProcessor Specifications for Cloud Ninjas' Intel Core Ultra Edition Compact Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eThe CPU is the central component in a compact Intel Core Ultra workstation. High single-core performance ensures responsive application behavior, smooth multitasking, and efficient handling of lightly threaded professional workloads. The Core Ultra architecture also emphasizes power efficiency, helping maintain sustained performance within thermally constrained small-form-factor systems.\u003c\/p\u003e\n\u003c\/div\u003e\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\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"subsection gpu-subsection\"\u003e\n\u003cdiv id=\"gpu-subsection-toggle\" class=\"sub-section-head\"\u003e\u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas' Intel Core Ultra Edition Compact Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eThe GPU plays a supporting but important role in a compact workstation. Integrated or entry-level discrete graphics provide smooth high-resolution display output, multi-monitor support, and acceleration for light visual workloads. While not intended for heavy rendering, a capable GPU improves UI responsiveness, media playback, and overall system balance in space-efficient professional deployments.\n\n\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"table-section\"\u003e\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 RTX PRO 4000 Blackwell\u003c\/td\u003e\n \u003ctd\u003e24GB GGDR7\u003c\/td\u003e\n \u003ctd\u003e1590 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 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\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\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Silent Owl Liquid Cooled","offer_id":42107856584750,"sku":"Cloud Ninjas WIUG-2N2S-1G-LC-Intel-Core-Ultra-Edition-Compact Size","price":1129.99,"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-workstations-for-nvidia-studio-intel-ultra-edition","title":"Cloud Ninjas Workstations for NVIDIA Studio Intel Ultra Edition","description":"\u003cdiv class=\"subsection cpu-subsection\"\u003e\n\u003cdiv id=\"cpu-subsection-toggle\" class=\"sub-section-head\"\u003e\u003cspan class=\"sub-section-tl\"\u003eProcessor Specifications for Cloud Ninjas' Intel Ultra Edition NVIDIA Studio Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eCreative applications benefit from strong single-core performance for interactive editing tasks and multi-core scalability for rendering, encoding, and AI-assisted processing. The Intel Core Ultra Edition workstation’s modern high-frequency, multi-core processor ensures responsive timeline performance, fast exports, and efficient handling of parallel workloads across professional creative software.\n\u003c\/p\u003e\n\u003c\/div\u003e\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\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"subsection gpu-subsection\"\u003e\n\u003cdiv id=\"gpu-subsection-toggle\" class=\"sub-section-head\"\u003e\u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas' Intel Ultra Edition NVIDIA Studio Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eGPU acceleration is central to NVIDIA Studio workflows, powering real-time rendering, AI effects, video encoding\/decoding, and advanced visual processing. The Intel Core Ultra Edition workstation’s professional NVIDIA GPU with expanded VRAM enhances playback smoothness, accelerates GPU-optimized rendering engines, and enables advanced AI features, ensuring high-performance results across modern creative applications.\n\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"table-section\"\u003e\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 RTX PRO 4000 Blackwell\u003c\/td\u003e\n \u003ctd\u003e24GB GGDR7\u003c\/td\u003e\n \u003ctd\u003e1590 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 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 \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\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Neon Fox Liquid Cooled","offer_id":42109789732910,"sku":"Cloud Ninjas WIUG-2N2S-1G-LC-Intel-Ultra-Edition-NVIDIA Studio","price":1809.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Cloud_Ninjas_WIUU-5N4S-2G_Liquid_Cooled.jpg?v=1776692291"},{"product_id":"cloud-ninjas-workstations-for-solidworks-simulation-intel-edition","title":"Cloud Ninjas Workstations for SolidWorks Simulation Intel Edition","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' Workstation for SolidWorks Simulation \u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eCPU performance is central to SolidWorks Simulation. The Intel Xeon W7-3565X provides a balanced architecture with strong multi-core performance for simulation solvers while leveraging high memory bandwidth from its platform. While simulation scaling typically plateaus beyond 12–16 cores, the Xeon platform’s memory throughput and stability make it ideal for handling large datasets and complex engineering calculations.\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' SolidWorks Simulation\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \t\u003cp\u003eGPU requirements in SolidWorks Simulation focus on stability and certified performance rather than raw compute alone. An RTX PRO 6000 Max-Q Workstation Edition ensures ISV-certified drivers for features like RealView and Order Independent Transparency (OIT), providing stable visualization of complex assemblies. Its large VRAM capacity also supports smooth interaction with detailed models and accelerates rendering workflows in tools like SolidWorks Visualize.\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":42282472374318,"sku":"Cloud Ninjas WIW35U-5N6S-4G-Intel-Edition-SolidWorks Simulation","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-workstations-for-siemens-nx-threadripper-edition","title":"Cloud Ninjas Workstations for Siemens NX Threadripper Edition","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' Workstation for Siemens NX\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eCPU performance in Siemens NX must balance high clock speed and multi-core capability. The AMD Ryzen Threadripper PRO 9975WX delivers strong single-core performance for responsive modeling and design tasks, while its high core count supports multi-threaded CAE simulations and data processing. Additionally, its platform provides high memory bandwidth and capacity, which are critical for large-scale engineering workflows.\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' Workstation for Siemens NX\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eGPU performance in Siemens NX is focused on stability, accuracy, and handling complex 3D models. An RTX PRO 6000 Max-Q Workstation Edition provides ISV-certified drivers to ensure reliability and geometric accuracy in professional environments. Its large VRAM capacity enables smooth interaction with massive assemblies, while advanced GPU architecture accelerates real-time rendering, visualization, and AI-assisted design features in modern NX workflows.\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":42283000234030,"sku":"Cloud Ninjas WATRG-4N8S-3G-Threadripper-Edition-Siemens NX","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-workstations-for-dassault-systemes-catia","title":"Cloud Ninjas Workstations for Dassault Systèmes CATIA","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' Workstation for Dassault Systèmes CATIA\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eCPU performance in CATIA depends on balancing high clock speeds and multi-core capability. The AMD Ryzen Threadripper PRO 9975WX delivers strong single-core performance for responsive modeling and interactive design tasks, while its high core count supports multi-threaded simulation workloads such as FEA and CFD. Its platform also provides high memory bandwidth, which is critical for handling large-scale engineering projects.\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' Workstation for Dassault Systèmes CATIA\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eGPU performance in CATIA is centered on stability and certified accuracy. An RTX PRO 6000 Max-Q Workstation Edition ensures ISV-certified drivers for reliable OpenGL performance and compatibility with Dassault Systèmes’ certified hardware standards. Its large VRAM capacity enables smooth interaction with massive assemblies, while advanced GPU architecture supports high-performance visualization and real-time rendering in professional CAD workflows.\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":42283038146606,"sku":"Cloud Ninjas WATRG-4N8S-3G-Threadripper-Edition-CATIA","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-workstations-for-matlab-threadripper-edition","title":"Cloud Ninjas Workstations for MATLAB Threadripper Edition","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' Workstation for MATLAB\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eCPU performance is a defining factor in MATLAB, especially for parallel computing and large-scale simulations. The AMD Threadripper PRO 9995WX, with its extreme core count and workstation-class architecture, excels in highly parallel workloads such as Monte Carlo simulations, PDE solvers, and large dataset processing. While MATLAB also benefits from strong single-core performance, this CPU’s massive multi-threaded throughput significantly accelerates compute-heavy workflows and parallel toolbox operations.\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' Workstation for MATLAB\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eGPU acceleration enhances performance in MATLAB for supported workloads such as deep learning, matrix operations, and GPU-parallelized algorithms. An RTX PRO 6000 Max-Q Workstation Edition provides exceptional VRAM capacity and compute throughput, enabling efficient processing of large datasets and complex GPU-accelerated computations. This ensures high-performance execution of modern MATLAB toolboxes that leverage CUDA-based acceleration.\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":42283075731502,"sku":"Cloud Ninjas WATRG-4N8S-3G-Threadripper-Edition-MATLAB","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-workstations-for-gnu-octave-threadripper-edition","title":"Cloud Ninjas Workstations for GNU Octave Threadripper Edition","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' Workstation for GNU Octave\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eCPU performance is a key driver in GNU Octave. The AMD Threadripper PRO 7965WX delivers strong multi-core performance, making it well-suited for parallel numerical computation, linear algebra operations, and large-scale simulation tasks. While GNU Octave benefits from high core counts, memory bandwidth and balanced per-core performance remain critical for solver efficiency and data throughput.\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' Workstation for GNU Octave\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eGPU acceleration can enhance performance in GNU Octave when using compatible libraries for matrix acceleration and scientific computing. The RTX PRO 6000 Max-Q Workstation Edition provides large VRAM capacity and professional-grade stability, making it suitable for GPU-assisted computation and high-precision numerical workloads. Its ECC-enabled workstation architecture supports improved reliability for long-running scientific calculations and data-intensive processing tasks.\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":42283168464942,"sku":"Cloud Ninjas WATRG-4N8S-3G-Threadripper-Edition-Octave","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-workstations-for-ansys-discovery-intel-edition","title":"Cloud Ninjas Workstations for Ansys Discovery Intel Edition","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' Workstation for Ansys Discovery\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eCPU performance plays a supporting but important role in Ansys Discovery. The Intel Xeon W-3565X provides strong multi-core performance and high memory bandwidth, which benefits preprocessing, geometry handling, and refinement stages. While Discovery primarily utilizes a limited number of cores for certain operations, a higher core count improves overall system responsiveness and parallel workload handling.\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' Workstation for Ansys Discovery\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \t\u003cp\u003eGPU acceleration is the primary performance driver in Ansys Discovery. An RTX PRO 6000 Max-Q Workstation Edition delivers massive VRAM capacity and advanced compute performance, enabling real-time simulation, fluid dynamics visualization, and instant design feedback. High VRAM ensures complex models and simulations remain fully resident on the GPU, eliminating bottlenecks and providing a fluid, interactive engineering workflow.\u003c\/p\u003e\n\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":42283249270830,"sku":"Cloud Ninjas WIW35U-5N6S-4G-Intel-Edition-Ansys Discovery","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-workstations-for-openfoam-epyc-edition","title":"Cloud Ninjas Workstations for OpenFOAM EPYC Edition","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' Workstation for OpenFOAM\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eCPU performance is the primary driver of simulation speed in OpenFOAM. The AMD EPYC 9655 excels due to its massive core count and, more importantly, its high number of memory channels, which dramatically improves parallel scaling efficiency. Unlike consumer CPUs, this architecture minimizes memory bottlenecks in large CFD cases, making it ideal for professional and research-grade simulations.\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' Workstation for OpenFOAM\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \t\u003cp\u003eGPU performance in OpenFOAM is primarily leveraged for accelerated solvers and post-processing. An RTX PRO 6000 Max-Q Workstation Edition provides high VRAM capacity and strong double-precision capabilities, making it suitable for GPU-accelerated CFD workflows when supported. It also ensures smooth visualization of complex simulation results in tools like ParaView, enabling efficient analysis of large-scale fluid dynamics datasets.\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":42283268079662,"sku":"Cloud Ninjas WAEG-5N6S-4G-EPYC-Edition-OpenFOAM","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"}],"url":"https:\/\/cloudninjas.com\/collections\/cloud-ninjas-workstations-for-high-powered-computing\/sw-cooling-lc.oembed","provider":"Cloud Ninjas","version":"1.0","type":"link"}