{"title":"Cloud Ninjas Workstations for Generative AI","description":"\u003cdiv class=\"seo-sections\"\u003e\n\n  \u003csection class=\"seo-section fade-in\"\u003e\n    \u003cdiv class=\"seo-content\"\u003e\n      \u003ch2\u003eHigh-Performance Workstations for Generative AI\u003c\/h2\u003e\n      \u003cp\u003e\n        Generative AI workloads can place significant demands on your GPU, VRAM, system memory, and storage. Cloud Ninjas workstations are built for demanding AI workflows including Stable Diffusion, FLUX, ComfyUI, and other GPU-accelerated applications. With the right combination of graphics performance and memory capacity, you can generate higher-resolution images, work with larger models, and iterate through creative workflows more efficiently.\n      \u003c\/p\u003e\n    \u003c\/div\u003e\n\n    \u003cdiv class=\"seo-image\"\u003e\n      \u003cimg src=\"https:\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/ai-art-img.webp?v=1774362616\" alt=\"AI-generated artwork created with a generative AI workstation\" width=\"700\" height=\"auto\" loading=\"lazy\"\u003e\n    \u003c\/div\u003e\n  \u003c\/section\u003e\n\n\n  \u003csection class=\"seo-section fade-in\"\u003e\n    \u003cdiv class=\"seo-content\"\u003e\n      \u003ch2\u003eBuilt for GPU-Intensive AI Workloads\u003c\/h2\u003e\n      \u003cp\u003e\n        GPU performance and VRAM are critical when working with demanding generative AI models. Higher memory capacity can help accommodate larger models, higher-resolution generation, and more complex workflows, while fast NVMe storage helps reduce model and dataset loading times. Our workstations can be configured with the processing power, memory, storage, and cooling needed for sustained AI workloads and evolving local AI applications.\n      \u003c\/p\u003e\n    \u003c\/div\u003e\n\n    \u003cdiv class=\"seo-image\"\u003e\n      \u003cimg src=\"https:\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/neural_network_img.webp?v=1774362861\" alt=\"Neural network visualization representing GPU-accelerated AI processing\" width=\"600\" height=\"auto\" loading=\"lazy\"\u003e\n    \u003c\/div\u003e\n  \u003c\/section\u003e\n\n\n  \u003csection class=\"seo-section fade-in\"\u003e\n    \u003cdiv class=\"seo-content\"\u003e\n      \u003ch2\u003eWorkstations for Stable Diffusion, FLUX, and ComfyUI\u003c\/h2\u003e\n      \u003cp\u003e\n        Create and refine AI-generated images with hardware designed for modern generative AI software. Whether you are building node-based workflows in ComfyUI, generating images with Stable Diffusion, experimenting with FLUX, or using AI-powered enhancement tools such as Topaz Studio, UniFab, and Upscayl, a capable workstation provides the GPU resources needed for demanding creative workflows.\n      \u003c\/p\u003e\n    \u003c\/div\u003e\n\n    \u003cdiv class=\"seo-image\"\u003e\n      \u003cimg src=\"https:\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Before-and-after_upscaling_example_using_Topaz_tools.webp?v=1774363467\" alt=\"Before-and-after AI image upscaling example using generative AI tools\" width=\"600\" height=\"auto\" loading=\"lazy\"\u003e\n    \u003c\/div\u003e\n  \u003c\/section\u003e\n\n\n  \u003csection class=\"seo-section fade-in\"\u003e\n    \u003cdiv class=\"seo-content\"\u003e\n      \u003ch2\u003eLocal AI Workstations for Creators and Professionals\u003c\/h2\u003e\n      \u003cp\u003e\n        Generative AI is not limited to image creation. A properly configured workstation can also run local AI models, experiment with large language models, and support AI-powered image and video enhancement. Applications such as LM Studio and LocalAI make it possible to run supported models locally, while high-performance GPUs, ample system memory, and fast storage provide the foundation for demanding AI experimentation and content creation.\n      \u003c\/p\u003e\n    \u003c\/div\u003e\n\n    \u003cdiv class=\"seo-image\"\u003e\n      \u003cimg src=\"https:\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Ultra-high-resolution_AI-generated_artwork_on_a_large_screen.webp?v=1774363720\" alt=\"High-resolution AI-generated artwork displayed on a professional workstation\" width=\"600\" height=\"auto\" loading=\"lazy\"\u003e\n    \u003c\/div\u003e\n  \u003c\/section\u003e\n\n\u003c\/div\u003e","products":[{"product_id":"cloud-ninjas-workstations-for-topaz-labs-topaz-studio-intel-xeon-edition","title":"Cloud Ninjas Workstations for Topaz Labs Topaz Studio 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's Topaz Studio Intel Xeon Edition Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eCPU performance remaqins important for media decoding, encoding, file processing, application responsiveness, and workloads that do not run entirely on the GPU. However, GPU accelaration is a major factor in Topaz AI processing particularly for demanding image and video enhancement models.\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's Topaz Studio Intel Xeon Edition Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eGPU perfotmance and VRAM are critical hardware considerations for local Topaz AI processing. Topaz applications use the GPU to accelerate AI powererd image and video enhancement, and available VRAM affects which mdels and processing workloads can run efficiently.\u003c\/p\u003e\n  \u003cp\u003e\n    Topaz's current windows recommendations incluide 8GB of VRAM for higher-perfomance Topaz Photo and Topaz Video configurations. Specific generative models can require more: Topaz documents requirements ranging from 10GB to 16GB or more depending on the model and processing mode.\n  \u003c\/p\u003e\n  \u003cp\u003e\n    For a workstation intended to run multiple Topaz applications or demanding enhancemnent modcels, higher VRAM GPUs provide additional headroom for large images, high-resolution video, and memory-intensive AI models.\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           \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 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 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":42077395157038,"sku":"Cloud Ninjas WIW35U-5N6S-4G-Intel-Xeon-Edition-Topaz Studio","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-generative-ai-amd-ryzen-edition","title":"Cloud Ninjas Workstations for Generative AI 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's Generative AI AMD Ryzen Edition Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eIn Generative AI workflows, the CPU primarily manages data preprocessing, prompt orchestration, tokenization, and pipeline coordination. Strong single-core performance improves responsiveness in scripting and inference control, while multi-threaded performance accelerates data loading, batching, and parallel preprocessing tasks. Although most heavy computation is GPU-dependent, an underpowered CPU can starve GPUs of data, making balanced multi-core performance essential for maintaining peak workstation efficiency.\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's Generative AI AMD Ryzen Edition Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eGenerative AI workloads are heavily GPU-dependent, particularly for training and running diffusion models, large language models, and real-time AI inference. High VRAM capacity is critical for fitting larger models, higher batch sizes, and longer context windows directly in memory. Multi-GPU configurations further improve scalability by distributing compute workloads and increasing throughput. A workstation-class GPU subsystem with strong tensor and compute performance is essential for delivering the real-time responsiveness and accelerated training speeds required in professional generative AI workflows.\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\u003eBase Clock\u003c\/th\u003e\n\u003cth\u003eBoost 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\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 Primal Gorilla","offer_id":42077489889326,"sku":"Cloud Ninjas WIW35U-5N6S-4G-Ryzen-Edition-Generative AI","price":1939.99,"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-black-forest-labs-amd-ryzen-edition","title":"Cloud Ninjas Workstations for Black Forest Labs FLUX 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's FLUX AMD Ryzen Edition Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eFLUX.2 benefits from CPUs with strong single-core performance and moderate to high core counts. While the model itself is not CPU-bound, multithreading improves performance during data preprocessing, prompt batching, concurrent inference jobs, and overall system responsiveness when running multiple AI tools simultaneously.\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's FLUX AMD Ryzen Edition Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eGPU performance and VRAM are important considerations for local FLUX.2 workflows. Image generation relies heavily on GPU compute, while available VRAM can influence which model configurations, quantization methods, and image generation workflows can be run locally. A modern GPU with high compute throughput and ample VRAM significantly improves generation speed, supports higher resolutions, enables larger batch sizes, and allows advanced workflows such as upscaling, image-to-image generation, and multi-model experimentation.\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\u003eBase Clock\u003c\/th\u003e\n\u003cth\u003eBoost 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\t  \u003ctr\u003e\n\t    \u003ctd\u003eNVIDIA RTX PRO 4000 Blackwell\u003c\/td\u003e\n\t    \u003ctd\u003e24GB GGDR7\u003c\/td\u003e\n\t\t\u003ctd\u003e1590 MHz\u003c\/td\u003e\n\t\t\u003ctd\u003e1750 MHz\u003c\/td\u003e\n\t\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 Steel Wolf","offer_id":42077527834670,"sku":"Cloud Ninjas WIW35U-5N6S-4G-Ryzen-Edition-Generative AI","price":1939.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Define_7-WARU-4N4S-2G-8.jpg?v=1771954998"},{"product_id":"cloud-ninjas-workstations-for-stable-diffusion-multi-gpu-edition","title":"Cloud Ninjas Workstations for Stable Diffusion Multi GPU 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' Multi GPU Edition Stable Diffusion Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eStable Diffusion is primarily GPU-accelerated, but the CPU still plays an important supporting role in data preparation, prompt processing, image encoding\/decoding, and multi-instance orchestration. The application does not scale heavily with extreme core counts, but it benefits from strong multi-threaded performance when running multiple jobs, managing complex ComfyUI graphs, or handling parallel preprocessing tasks. This workstation’s CPU configuration is selected to maintain balanced system throughput so the GPUs remain fully utilized during professional generative workflows.\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' Multi GPU Edition Stable Diffusion Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eStable Diffusion performance is heavily GPU-dependent, with VRAM capacity and tensor compute performance acting as primary bottlenecks. Modern diffusion models often require 12–24GB+ of VRAM for high-resolution or multi-adapter workflows. \u003cstrong\u003eWhile most Stable Diffusion pipelines do not pool VRAM across GPUs, they scale extremely well by running concurrent jobs per GPU, dramatically increasing total workstation throughput for professional AI production environments.\u003c\/strong\u003e\u003c\/p\u003e\n  \u003cp\u003e\n    Multi-GPU configurations are particularly useful for AI studios, content-production teams, research environments, and developers testing multiple generation workflows concurrently. Instead of waiting for one GPU to finish each job, multiple accelators can work on seperate requests at the same time.\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\n\u003cdiv class=\"subsection gpu-subsection\"\u003e\n  \u003ch2\u003eWhy Choose a Multi-GPU Stable Diffusion Workstation?\u003c\/h2\u003e\n  \u003cp\u003e\n    The Standard Edition is designed around a powerful single-GPU configuration for interactive Stable Diffusion generation, creative work, and model experimentation. The Multi-GPU Edition is designed for a different requirement: maximizing generation throughput.\nStableSwarmUI supports multiple GPU backends and distributes queued generation requests across them, allowing multiple images to be generated concurrently. This makes multiple GPUs particularly valuable when the objective is to process more generation jobs in parallel rather than simply improving the performance of one individual generation.\n  \u003c\/p\u003e\n  \u003cp\u003e\n    The Multi-GPU Edition combines this GPU-level parallelism with an AMD Ryzen Threadripper PRO platform that provides substantial PCIe connectivity and memory bandwidth for multiple accelerators. AMD's Threadripper PRO 9000 WX platform provides up to 128 PCIe 5.0 lanes specifically suited to advanced multi-GPU and local AI configurations.\n  \u003c\/p\u003e\n  \u003cp\u003e\n    Choose the Standard Edition for single-GPU creative workflows and interactive generation. Choose the Multi-GPU Edition when generation throughput, concurrent workloads, and multiple GPU backends are the priority.\n  \u003c\/p\u003e\n\u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Mythical Dragon","offer_id":42088387837998,"sku":"Cloud Ninjas WATRU-6N4S-4G-Multi-GPU-Edition-Stable Diffusion","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-stable-diffusion-standard-edition","title":"Cloud Ninjas Workstations for Stable Diffusion Standard 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's Standard Edition Stable Diffusion Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eWhile image generation is primarily GPU-driven, the CPU plays an important role in data preparation, prompt processing, image decoding, and workflow coordination. The Standard Edition workstation’s high-frequency multi-core processor ensures efficient pipeline management, responsive UI interaction, and smooth handling of background tasks that support AI generation workflows.\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's Standard Edition Stable Diffusion Workstation\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eStable Diffusion performance is heavily dependent on GPU compute capability and available VRAM. The Standard Edition workstation’s professional GPU with ample VRAM significantly improves image generation speed, enables higher-resolution outputs, supports larger batch sizes, and allows the use of more advanced models and extensions. A capable GPU is the primary driver of performance for modern diffusion-based AI 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                 \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 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                 \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  \u003cdiv class=\"sub-section-head\"\u003e\u003cspan class=\"sub-section-tl\"\u003eStandard VS Multi-GPU\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv class=\"subsection-information\"\u003e\n\u003cp\u003eThe Standard Edition is designed for high-performance single-GPU Stable Diffusion workloads. It is the appropriate configuration for users who primarily generate images interactively, develop creative projects, experiment with models, or run individual AI workflows.\u003c\/p\u003e\n  \u003cp\u003eA multi-GPU configuration serves a different purpose. Multiple GPUs become particularly valuable when the workload can distribute generation requests across separate GPU backends or when users need to process multiple generations concurrently. Stability AI's StableSwarmUI documentation describes using multiple GPU backends to distribute queued generation requests across GPUs, with additional GPUs handling additional simultaneous generations.\u003c\/p\u003e\n  \u003cp\u003e\n    For this reason, the Standard Edition prioritizes a powerful single-GPU configuration and a conventional Ryzen platform, while the Multi-GPU Edition is designed for higher-throughput AI production environments where multiple accelerators and additional PCIe expansion are justified.\n  \u003c\/p\u003e\n\u003c\/div\u003e\n\n","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Steel Wolf Liquid Cooled","offer_id":42109825122350,"sku":"Cloud Ninjas WARU-4N4S-2G-LC-Standard-Edition-Stable Diffusion","price":1929.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Cloud_Ninjas_WARU-4N4S-2G_Liquid_Cooled_1.jpg?v=1775674169"},{"product_id":"cloud-ninjas-workstations-for-comfyui-intel-edition","title":"Cloud Ninjas Workstations for ComfyUI 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' ComfyUI Workstation\u003c\/span\u003e\n        \u003c\/div\u003e\n\n        \u003cdiv class=\"subsection-information\"\u003e\n            \u003cp\u003eComfyUI benefits from a balance CPU with strong multi-threading capabilities for handling pre-processing tasks, node execution coordination, and data transfers betweeen components. While GPU acceleration dominates generations, CPU performance impacts workflow responsiveness, image formatting, and VRAM management especially when running multiple pipelines or managing large batches.\u003c\/p\u003e\n        \u003c\/div\u003e\n\n    \u003c\/div\u003e\n    \u003cdiv class=\"table-section\"\u003e\n        \u003cdiv class=\"cpu-compatibility-table\"\u003e\n            \u003ctable class=\"data-table\"\u003e\n                \u003cthead\u003e\n                    \u003ctr\u003e\n                        \u003cth\u003eCPU\u003c\/th\u003e\n                        \u003cth\u003eCores \u0026amp; Threads\u003c\/th\u003e\n                        \u003cth\u003eBase Clock\u003c\/th\u003e\n                        \u003cth\u003eTurbo Clock\u003c\/th\u003e\n                    \u003c\/tr\u003e\n                \u003c\/thead\u003e\n                \u003ctbody\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 9 285T\u003c\/td\u003e\n                        \u003ctd\u003e24C\/24T\u003c\/td\u003e\n                        \u003ctd\u003e1.40 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.40 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 9 285K\u003c\/td\u003e\n                        \u003ctd\u003e24C\/24T\u003c\/td\u003e\n                        \u003ctd\u003e3.70 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.70 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 9 285\u003c\/td\u003e\n                        \u003ctd\u003e24C\/24T\u003c\/td\u003e\n                        \u003ctd\u003e2.50 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.60 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 7 265T\u003c\/td\u003e\n                        \u003ctd\u003e20C\/20T\u003c\/td\u003e\n                        \u003ctd\u003e1.50 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 7 265KF\u003c\/td\u003e\n                        \u003ctd\u003e20C\/20T\u003c\/td\u003e\n                        \u003ctd\u003e3.90 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                   \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 7 265K\u003c\/td\u003e\n                        \u003ctd\u003e20C\/20T\u003c\/td\u003e\n                        \u003ctd\u003e3.90 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 7 265F\u003c\/td\u003e\n                        \u003ctd\u003e20C\/20T\u003c\/td\u003e\n                        \u003ctd\u003e2.40 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 7 265\u003c\/td\u003e\n                        \u003ctd\u003e20C\/20T\u003c\/td\u003e\n                        \u003ctd\u003e2.40 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 5 245KF\u003c\/td\u003e\n                        \u003ctd\u003e14C\/14T\u003c\/td\u003e\n                        \u003ctd\u003e4.20 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.20 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 5 245K\u003c\/td\u003e\n                        \u003ctd\u003e14C\/14T\u003c\/td\u003e\n                        \u003ctd\u003e4.20 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.20 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 5 245\u003c\/td\u003e\n                        \u003ctd\u003e14C\/14T\u003c\/td\u003e\n                        \u003ctd\u003e3.50 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.10 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 5 235T\u003c\/td\u003e\n                        \u003ctd\u003e14C\/14T\u003c\/td\u003e\n                        \u003ctd\u003e2.30 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.00 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 5 235\u003c\/td\u003e\n                        \u003ctd\u003e14C\/14T\u003c\/td\u003e\n                        \u003ctd\u003e3.40 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.00 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 5 225\u003c\/td\u003e\n                        \u003ctd\u003e10C\/10T\u003c\/td\u003e\n                        \u003ctd\u003e2.50 GHz\u003c\/td\u003e\n                        \u003ctd\u003e4.90 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                    \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 5 225F\u003c\/td\u003e\n                        \u003ctd\u003e10C\/10T\u003c\/td\u003e\n                        \u003ctd\u003e3.30 GHz\u003c\/td\u003e\n                        \u003ctd\u003e4.90 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 5 225\u003c\/td\u003e\n                        \u003ctd\u003e10C\/10T\u003c\/td\u003e\n                        \u003ctd\u003e3.30 GHz\u003c\/td\u003e\n                        \u003ctd\u003e4.90 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                \u003c\/tbody\u003e\n            \u003c\/table\u003e\n\n        \u003c\/div\u003e\n    \u003c\/div\u003e\n\n    \u003cdiv class=\"subsection gpu-subsection\"\u003e\n        \u003cdiv class=\"sub-section-head\" id=\"gpu-subsection-toggle\"\u003e\n            \u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas WIUU-5N4S-2G\u003c\/span\u003e\n        \u003c\/div\u003e\n\n        \u003cdiv class=\"subsection-information\"\u003e\n          \t\u003cp\u003eGPU acceleration is the primary driver of ComfyUI performance, as image generation relies heavily on deep learning inference. A workstation GPU with high compute performance and substantial VRAM is critical for handling large diffusion models, high-resolution outputs, and advanced features like ControlNet or upscaling. More VRAM enables larger batch sizes and more complex workflows without memory constraints, directly improving efficiency in professional AI-driven content creation.\n\u003c\/p\u003e\n        \u003c\/div\u003e\n\n    \u003c\/div\u003e\n\n    \u003cdiv class=\"table-section\"\u003e\n\n        \u003cdiv class=\"gpu-compatibility-table\"\u003e\n            \u003ctable class=\"data-table\"\u003e\n                \u003cthead\u003e\n                    \u003ctr\u003e\n                        \u003cth\u003eGPU\u003c\/th\u003e\n                        \u003cth\u003eVRAM\u003c\/th\u003e\n                        \u003cth\u003eGPU Clock\u003c\/th\u003e\n                        \u003cth\u003eMemory Clock\u003c\/th\u003e\n                    \u003c\/tr\u003e\n                \u003c\/thead\u003e\n                \u003ctbody\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Workstation Edition\u003c\/td\u003e\n                        \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2617 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Max Q Workstation Edition\u003c\/td\u003e\n                        \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e2280 MHz\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX PRO 5000 Blackwell\u003c\/td\u003e\n                        \u003ctd\u003e48GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e2377 MHz\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX PRO 4500 Blackwell\u003c\/td\u003e\n                        \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e2407 MHz\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA 6000 ADA Generation\u003c\/td\u003e\n                        \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n                        \u003ctd\u003e2505 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2500 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 5090\u003c\/td\u003e\n                        \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2407 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 5000 ADA Generation\u003c\/td\u003e\n                        \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n                        \u003ctd\u003e2550 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2250 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 4500 ADA Generation\u003c\/td\u003e\n                        \u003ctd\u003e24GB GDDR6\u003c\/td\u003e\n                        \u003ctd\u003e2580 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2250 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 4000 ADA Generation\u003c\/td\u003e\n                        \u003ctd\u003e20GB GDDR6\u003c\/td\u003e\n                        \u003ctd\u003e2175 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2250 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 5080\u003c\/td\u003e\n                        \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e1875 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2617 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 5070 Ti\u003c\/td\u003e\n                        \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2452 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 5070\u003c\/td\u003e\n                        \u003ctd\u003e12GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2512 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                    \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 5060 Ti\u003c\/td\u003e\n                        \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2572 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX A1000\u003c\/td\u003e\n                        \u003ctd\u003e8GB GDDR6\u003c\/td\u003e\n                        \u003ctd\u003e1462 MHz\u003c\/td\u003e\n                        \u003ctd\u003e1500 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX A400\u003c\/td\u003e\n                        \u003ctd\u003e4GB GDDR6\u003c\/td\u003e\n                        \u003ctd\u003e1762 MHz\u003c\/td\u003e\n                        \u003ctd\u003e1500 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                 \n                \u003c\/tbody\u003e\n            \u003c\/table\u003e\n\n            \n        \u003c\/div\u003e\n    \u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Neon Fox Air Cooled","offer_id":42231227842606,"sku":"Cloud Ninjas WIUU-5N4S-2G-AC-Intel-Edition-ComfyUI","price":1829.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Define_7-WIUU-5N4S-2G-8.jpg?v=1771955738"},{"product_id":"cloud-ninjas-workstations-for-lmstudio-intel-edition","title":"Cloud Ninjas Workstations for LM Studio 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' LM Studio Workstation\u003c\/span\u003e\n        \u003c\/div\u003e\n\n        \u003cdiv class=\"subsection-information\"\u003e\n            \u003cp\u003e\n              CPU performance remains important for LM Studio, particularly for CPU-based inference, model loading, data preparation\/preprocessing, and workloads that use CPU and GPU resources together. The appropriate processor depends on the models being used and the applications running alongsides LM Studio. \n            \u003c\/p\u003e\n          \u003cp\u003e\n            A capable multi-core processor can also provide additional headroom when using LM Studion alongside development tools, APIs, productivity applications, or other local AI software.\n          \u003c\/p\u003e\n        \u003c\/div\u003e\n\n    \u003c\/div\u003e\n    \u003cdiv class=\"table-section\"\u003e\n        \u003cdiv class=\"cpu-compatibility-table\"\u003e\n            \u003ctable class=\"data-table\"\u003e\n                \u003cthead\u003e\n                    \u003ctr\u003e\n                        \u003cth\u003eCPU\u003c\/th\u003e\n                        \u003cth\u003eCores \u0026amp; Threads\u003c\/th\u003e\n                        \u003cth\u003eBase Clock\u003c\/th\u003e\n                        \u003cth\u003eTurbo Clock\u003c\/th\u003e\n                    \u003c\/tr\u003e\n                \u003c\/thead\u003e\n                \u003ctbody\u003e\n                    \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 9 285T\u003c\/td\u003e\n                        \u003ctd\u003e24C\/24T\u003c\/td\u003e\n                        \u003ctd\u003e1.40 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.40 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 9 285K\u003c\/td\u003e\n                        \u003ctd\u003e24C\/24T\u003c\/td\u003e\n                        \u003ctd\u003e3.70 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.70 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 9 285\u003c\/td\u003e\n                        \u003ctd\u003e24C\/24T\u003c\/td\u003e\n                        \u003ctd\u003e2.50 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.60 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 7 265T\u003c\/td\u003e\n                        \u003ctd\u003e20C\/20T\u003c\/td\u003e\n                        \u003ctd\u003e1.50 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 7 265KF\u003c\/td\u003e\n                        \u003ctd\u003e20C\/20T\u003c\/td\u003e\n                        \u003ctd\u003e3.90 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                   \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 7 265K\u003c\/td\u003e\n                        \u003ctd\u003e20C\/20T\u003c\/td\u003e\n                        \u003ctd\u003e3.90 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 7 265F\u003c\/td\u003e\n                        \u003ctd\u003e20C\/20T\u003c\/td\u003e\n                        \u003ctd\u003e2.40 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 7 265\u003c\/td\u003e\n                        \u003ctd\u003e20C\/20T\u003c\/td\u003e\n                        \u003ctd\u003e2.40 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 5 245KF\u003c\/td\u003e\n                        \u003ctd\u003e14C\/14T\u003c\/td\u003e\n                        \u003ctd\u003e4.20 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.20 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 5 245K\u003c\/td\u003e\n                        \u003ctd\u003e14C\/14T\u003c\/td\u003e\n                        \u003ctd\u003e4.20 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.20 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 5 245\u003c\/td\u003e\n                        \u003ctd\u003e14C\/14T\u003c\/td\u003e\n                        \u003ctd\u003e3.50 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.10 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 5 235T\u003c\/td\u003e\n                        \u003ctd\u003e14C\/14T\u003c\/td\u003e\n                        \u003ctd\u003e2.30 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.00 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 5 235\u003c\/td\u003e\n                        \u003ctd\u003e14C\/14T\u003c\/td\u003e\n                        \u003ctd\u003e3.40 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.00 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 5 225\u003c\/td\u003e\n                        \u003ctd\u003e10C\/10T\u003c\/td\u003e\n                        \u003ctd\u003e2.50 GHz\u003c\/td\u003e\n                        \u003ctd\u003e4.90 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                    \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 5 225F\u003c\/td\u003e\n                        \u003ctd\u003e10C\/10T\u003c\/td\u003e\n                        \u003ctd\u003e3.30 GHz\u003c\/td\u003e\n                        \u003ctd\u003e4.90 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 5 225\u003c\/td\u003e\n                        \u003ctd\u003e10C\/10T\u003c\/td\u003e\n                        \u003ctd\u003e3.30 GHz\u003c\/td\u003e\n                        \u003ctd\u003e4.90 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                \u003c\/tbody\u003e\n            \u003c\/table\u003e\n\n        \u003c\/div\u003e\n    \u003c\/div\u003e\n\n    \u003cdiv class=\"subsection gpu-subsection\"\u003e\n        \u003cdiv class=\"sub-section-head\" id=\"gpu-subsection-toggle\"\u003e\n            \u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas' LM Studio Workstation\u003c\/span\u003e\n        \u003c\/div\u003e\n\n        \u003cdiv class=\"subsection-information\"\u003e\n          \t\u003cp\u003eGPU acceleration significantly improves LM Studio performance for supported models. A workstation GPU with high compute performance and ample VRAM enables faster token generation, supports larger models without aggressive quantization, and improves responsiveness in real-time AI interactions. Increased VRAM capacity is especially important for maintaining performance with larger context sizes and concurrent workloads.\n\u003c\/p\u003e\n  \u003cp\u003e\n    GPU performance and available VRAM are important considerations when configuring a workstation for LM Studio. The amounf of VRAM required depends on the model and quantization being used. A workstation with greater GPU memory provides additional flexibility when working with large local language models, while system with less VRAM can still support many models through quantization and CPU or GPU offloading.\n  \u003c\/p\u003e\n          \u003cp\u003e\n            LM Studio supports a range of local model configurations, and GPU requirements vary according to model size, quantization, context length, and offloading configuration. Cloud Ninjas offers workstation configurations with different levels of GPU memory so users can select a system appropriate for their local AI workloads.\n          \u003c\/p\u003e\n        \u003c\/div\u003e\n\n    \u003c\/div\u003e\n\n    \u003cdiv class=\"table-section\"\u003e\n\n        \u003cdiv class=\"gpu-compatibility-table\"\u003e\n            \u003ctable class=\"data-table\"\u003e\n                \u003cthead\u003e\n                    \u003ctr\u003e\n                        \u003cth\u003eGPU\u003c\/th\u003e\n                        \u003cth\u003eVRAM\u003c\/th\u003e\n                        \u003cth\u003eGPU Clock\u003c\/th\u003e\n                        \u003cth\u003eMemory Clock\u003c\/th\u003e\n                    \u003c\/tr\u003e\n                \u003c\/thead\u003e\n                \u003ctbody\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Workstation Edition\u003c\/td\u003e\n                        \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2617 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Max Q Workstation Edition\u003c\/td\u003e\n                        \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e2280 MHz\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX PRO 5000 Blackwell\u003c\/td\u003e\n                        \u003ctd\u003e48GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e2377 MHz\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX PRO 4500 Blackwell\u003c\/td\u003e\n                        \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e2407 MHz\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA 6000 ADA Generation\u003c\/td\u003e\n                        \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n                        \u003ctd\u003e2505 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2500 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 5090\u003c\/td\u003e\n                        \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2407 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 5000 ADA Generation\u003c\/td\u003e\n                        \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n                        \u003ctd\u003e2550 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2250 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 4500 ADA Generation\u003c\/td\u003e\n                        \u003ctd\u003e24GB GDDR6\u003c\/td\u003e\n                        \u003ctd\u003e2580 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2250 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 4000 ADA Generation\u003c\/td\u003e\n                        \u003ctd\u003e20GB GDDR6\u003c\/td\u003e\n                        \u003ctd\u003e2175 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2250 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 5080\u003c\/td\u003e\n                        \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e1875 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2617 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 5070 Ti\u003c\/td\u003e\n                        \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2452 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 5070\u003c\/td\u003e\n                        \u003ctd\u003e12GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2512 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                    \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 5060 Ti\u003c\/td\u003e\n                        \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2572 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX A1000\u003c\/td\u003e\n                        \u003ctd\u003e8GB GDDR6\u003c\/td\u003e\n                        \u003ctd\u003e1462 MHz\u003c\/td\u003e\n                        \u003ctd\u003e1500 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX A400\u003c\/td\u003e\n                        \u003ctd\u003e4GB GDDR6\u003c\/td\u003e\n                        \u003ctd\u003e1762 MHz\u003c\/td\u003e\n                        \u003ctd\u003e1500 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                 \n                \u003c\/tbody\u003e\n            \u003c\/table\u003e\n\n            \n        \u003c\/div\u003e\n    \u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Ghost Viper Air Cooled","offer_id":42231379787822,"sku":"Cloud Ninjas WIUU-3N4S-1G-AC-Intel-Edition-LM Studio","price":1159.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Define_7_Mini-WIUU-3N4S-1G-1-AC.jpg?v=1771954927"},{"product_id":"cloud-ninjas-workstations-for-unifab-ryzen-edition","title":"Cloud Ninjas Workstations for UniFab Ryzen 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' UniFab Workstation\u003c\/span\u003e\n        \u003c\/div\u003e\n\n        \u003cdiv class=\"subsection-information\"\u003e\n            \u003cp\u003eCPU performance remains important for video conversion, encoding, decoding, application responsiveness, and workloads that are not fully accelerated by the GPU. UniFab uses different processing methosds depending on the feature being used, so CPU requirements can vary betweeen standard conversions tasks and AI-powered enhancement workflows.\u003c\/p\u003e\n          \u003cp\u003e\n            A capable multi-core AMD Ryzen processor provides additional processing headroom for demanding video workloads and for running UniFab alongside editing, encoding, or other production applications.\n          \u003c\/p\u003e\n        \u003c\/div\u003e\n\n    \u003c\/div\u003e\n    \u003cdiv class=\"table-section\"\u003e\n        \u003cdiv class=\"cpu-compatibility-table\"\u003e\n            \u003ctable class=\"data-table\"\u003e\n                \u003cthead\u003e\n                    \u003ctr\u003e\n                        \u003cth\u003eCPU\u003c\/th\u003e\n                        \u003cth\u003eCores \u0026amp; Threads\u003c\/th\u003e\n                        \u003cth\u003eBase Clock\u003c\/th\u003e\n                        \u003cth\u003eTurbo Clock\u003c\/th\u003e\n                    \u003c\/tr\u003e\n                \u003c\/thead\u003e\n                \u003ctbody\u003e\n                    \u003ctr\u003e\n                        \u003ctd\u003eAMD Ryzen 7 9700X\u003c\/td\u003e\n                        \u003ctd\u003e8C\/16T\u003c\/td\u003e\n                        \u003ctd\u003e3.80 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                    \u003ctr\u003e\n                        \u003ctd\u003eAMD Ryzen 7 9800X3D\u003c\/td\u003e\n                        \u003ctd\u003e8C\/16T\u003c\/td\u003e\n                        \u003ctd\u003e4.70 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.20 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                    \u003ctr\u003e\n                        \u003ctd\u003eAMD Ryzen 9 9900X\u003c\/td\u003e\n                        \u003ctd\u003e12C\/24T\u003c\/td\u003e\n                        \u003ctd\u003e4.40 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.60 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                    \u003ctr\u003e\n                        \u003ctd\u003eAMD Ryzen 9 9900X3D\u003c\/td\u003e\n                        \u003ctd\u003e12C\/24T\u003c\/td\u003e\n                        \u003ctd\u003e4.40 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eAMD Ryzen 9 9950X\u003c\/td\u003e\n                        \u003ctd\u003e16C\/32T\u003c\/td\u003e\n                        \u003ctd\u003e4.30 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.70 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eAMD Ryzen 9 9950X3D\u003c\/td\u003e\n                        \u003ctd\u003e16C\/32T\u003c\/td\u003e\n                        \u003ctd\u003e4.30 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.70 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                \u003c\/tbody\u003e\n            \u003c\/table\u003e\n\n        \u003c\/div\u003e\n    \u003c\/div\u003e\n\n    \u003cdiv class=\"subsection gpu-subsection\"\u003e\n        \u003cdiv class=\"sub-section-head\" id=\"gpu-subsection-toggle\"\u003e\n            \u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas' UniFab Workstation\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 UniFab Video Enhancer AI, as AI upscaling and enhancement models run on the GPU. A workstation GPU with high compute capability and ample VRAM is essential for handling high-resolution video, complex enhancement models, and batch processing tasks. Increased VRAM capacity enables smoother processing of 4K and 8K content and reduces limitations during intensive AI-driven video workflows.\n\u003c\/p\u003e\n        \u003c\/div\u003e\n\n    \u003c\/div\u003e\n\n    \u003cdiv class=\"table-section\"\u003e\n\n        \u003cdiv class=\"gpu-compatibility-table\"\u003e\n            \u003ctable class=\"data-table\"\u003e\n                \u003cthead\u003e\n                    \u003ctr\u003e\n                        \u003cth\u003eGPU\u003c\/th\u003e\n                        \u003cth\u003eVRAM\u003c\/th\u003e\n                        \u003cth\u003eGPU Clock\u003c\/th\u003e\n                        \u003cth\u003eMemory Clock\u003c\/th\u003e\n                    \u003c\/tr\u003e\n                \u003c\/thead\u003e\n                \u003ctbody\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Workstation Edition\u003c\/td\u003e\n                        \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2617 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Max Q Workstation Edition\u003c\/td\u003e\n                        \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e2280 MHz\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX PRO 5000 Blackwell\u003c\/td\u003e\n                        \u003ctd\u003e48GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e2377 MHz\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX PRO 4500 Blackwell\u003c\/td\u003e\n                        \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e2407 MHz\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA 6000 ADA Generation\u003c\/td\u003e\n                        \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n                        \u003ctd\u003e2505 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2500 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 5090\u003c\/td\u003e\n                        \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2407 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 5000 ADA Generation\u003c\/td\u003e\n                        \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n                        \u003ctd\u003e2550 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2250 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 4500 ADA Generation\u003c\/td\u003e\n                        \u003ctd\u003e24GB GDDR6\u003c\/td\u003e\n                        \u003ctd\u003e2580 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2250 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 4000 ADA Generation\u003c\/td\u003e\n                        \u003ctd\u003e20GB GDDR6\u003c\/td\u003e\n                        \u003ctd\u003e2175 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2250 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 5080\u003c\/td\u003e\n                        \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e1875 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2617 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 5070 Ti\u003c\/td\u003e\n                        \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2452 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 5070\u003c\/td\u003e\n                        \u003ctd\u003e12GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2512 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                    \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 5060 Ti\u003c\/td\u003e\n                        \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2572 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX A1000\u003c\/td\u003e\n                        \u003ctd\u003e8GB GDDR6\u003c\/td\u003e\n                        \u003ctd\u003e1462 MHz\u003c\/td\u003e\n                        \u003ctd\u003e1500 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX A400\u003c\/td\u003e\n                        \u003ctd\u003e4GB GDDR6\u003c\/td\u003e\n                        \u003ctd\u003e1762 MHz\u003c\/td\u003e\n                        \u003ctd\u003e1500 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                 \n                \u003c\/tbody\u003e\n            \u003c\/table\u003e\n\n            \n        \u003c\/div\u003e\n    \u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Steel Wolf Air Cooled","offer_id":42231410786350,"sku":"Cloud Ninjas WARU-4N4S-2G-AC-Ryzen-Edition-UniFab","price":1939.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Define_7-WARU-4N4S-2G-8.jpg?v=1771954998"},{"product_id":"cloud-ninjas-workstations-for-localai-intel-edition","title":"Cloud Ninjas Workstations for LocalAI 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' LocalAI Workstation\u003c\/span\u003e\n        \u003c\/div\u003e\n        \u003cdiv class=\"subsection-information\"\u003e\n            \u003cp\u003e\n              LocalAI can run models using CPU resourves, while supported backends can also use GPU accelaration. CPU requirements vary according to the model, backend, quantization, context size, and whether workloads are being processed concurrently. LocalAI can automatically detect available CPU capabilities and select optimized backend configurations fro supported workloads.\n            \u003c\/p\u003e\n        \u003c\/div\u003e\n\n    \u003c\/div\u003e\n    \u003cdiv class=\"table-section\"\u003e\n        \u003cdiv class=\"cpu-compatibility-table\"\u003e\n            \u003ctable class=\"data-table\"\u003e\n                \u003cthead\u003e\n                    \u003ctr\u003e\n                        \u003cth\u003eCPU\u003c\/th\u003e\n                        \u003cth\u003eCores \u0026amp; Threads\u003c\/th\u003e\n                        \u003cth\u003eBase Clock\u003c\/th\u003e\n                        \u003cth\u003eTurbo Clock\u003c\/th\u003e\n                    \u003c\/tr\u003e\n                \u003c\/thead\u003e\n                \u003ctbody\u003e\n                    \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 9 285T\u003c\/td\u003e\n                        \u003ctd\u003e24C\/24T\u003c\/td\u003e\n                        \u003ctd\u003e1.40 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.40 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 9 285K\u003c\/td\u003e\n                        \u003ctd\u003e24C\/24T\u003c\/td\u003e\n                        \u003ctd\u003e3.70 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.70 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 9 285\u003c\/td\u003e\n                        \u003ctd\u003e24C\/24T\u003c\/td\u003e\n                        \u003ctd\u003e2.50 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.60 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 7 265T\u003c\/td\u003e\n                        \u003ctd\u003e20C\/20T\u003c\/td\u003e\n                        \u003ctd\u003e1.50 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 7 265KF\u003c\/td\u003e\n                        \u003ctd\u003e20C\/20T\u003c\/td\u003e\n                        \u003ctd\u003e3.90 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                   \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 7 265K\u003c\/td\u003e\n                        \u003ctd\u003e20C\/20T\u003c\/td\u003e\n                        \u003ctd\u003e3.90 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 7 265F\u003c\/td\u003e\n                        \u003ctd\u003e20C\/20T\u003c\/td\u003e\n                        \u003ctd\u003e2.40 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 7 265\u003c\/td\u003e\n                        \u003ctd\u003e20C\/20T\u003c\/td\u003e\n                        \u003ctd\u003e2.40 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.30 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 5 245KF\u003c\/td\u003e\n                        \u003ctd\u003e14C\/14T\u003c\/td\u003e\n                        \u003ctd\u003e4.20 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.20 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 5 245K\u003c\/td\u003e\n                        \u003ctd\u003e14C\/14T\u003c\/td\u003e\n                        \u003ctd\u003e4.20 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.20 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 5 245\u003c\/td\u003e\n                        \u003ctd\u003e14C\/14T\u003c\/td\u003e\n                        \u003ctd\u003e3.50 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.10 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 5 235T\u003c\/td\u003e\n                        \u003ctd\u003e14C\/14T\u003c\/td\u003e\n                        \u003ctd\u003e2.30 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.00 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 5 235\u003c\/td\u003e\n                        \u003ctd\u003e14C\/14T\u003c\/td\u003e\n                        \u003ctd\u003e3.40 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.00 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 5 225\u003c\/td\u003e\n                        \u003ctd\u003e10C\/10T\u003c\/td\u003e\n                        \u003ctd\u003e2.50 GHz\u003c\/td\u003e\n                        \u003ctd\u003e4.90 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                    \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 5 225F\u003c\/td\u003e\n                        \u003ctd\u003e10C\/10T\u003c\/td\u003e\n                        \u003ctd\u003e3.30 GHz\u003c\/td\u003e\n                        \u003ctd\u003e4.90 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eIntel Ultra 5 225\u003c\/td\u003e\n                        \u003ctd\u003e10C\/10T\u003c\/td\u003e\n                        \u003ctd\u003e3.30 GHz\u003c\/td\u003e\n                        \u003ctd\u003e4.90 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                \u003c\/tbody\u003e\n            \u003c\/table\u003e\n\n        \u003c\/div\u003e\n    \u003c\/div\u003e\n\n    \u003cdiv class=\"subsection gpu-subsection\"\u003e\n        \u003cdiv class=\"sub-section-head\" id=\"gpu-subsection-toggle\"\u003e\n            \u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas' localAI Workstation\u003c\/span\u003e\n        \u003c\/div\u003e\n\n        \u003cdiv class=\"subsection-information\"\u003e\n          \t\u003cp\u003e\nGPU acceleration can improve inference performance for supported LocalAI backends, particularly for workloads that can offload model layers to the GPU. Available VRAM is an important consideration because LocalAI uses GPU memory for model workloads, and the amount required can vary with model architecture, quantication, context size, and GPU-layer offloading. LocalAI also provides tools for estimating model VRAM requirements and managing m ultiple models in memory.\n      \u003c\/p\u003e\n          \u003cp\u003e\n            LocalAI supports GPU acceleration across NVIDIA, AMD, and Intel hardware, with available backends and configuration depending on the workload. NVIDIA GPUs are particularly relevant to this wokrstaion lineup because of their available CUDA-based backends and broad range of VRAM capacities. For larger or concurrent workloads, additional GPU memory can proivde more room for model wights, context, and multiple active models.\n          \u003c\/p\u003e\n        \u003c\/div\u003e\n\n    \u003c\/div\u003e\n\n    \u003cdiv class=\"table-section\"\u003e\n\n        \u003cdiv class=\"gpu-compatibility-table\"\u003e\n            \u003ctable class=\"data-table\"\u003e\n                \u003cthead\u003e\n                    \u003ctr\u003e\n                        \u003cth\u003eGPU\u003c\/th\u003e\n                        \u003cth\u003eVRAM\u003c\/th\u003e\n                        \u003cth\u003eGPU Clock\u003c\/th\u003e\n                        \u003cth\u003eMemory Clock\u003c\/th\u003e\n                    \u003c\/tr\u003e\n                \u003c\/thead\u003e\n                \u003ctbody\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Workstation Edition\u003c\/td\u003e\n                        \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2617 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Max Q Workstation Edition\u003c\/td\u003e\n                        \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e2280 MHz\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX PRO 5000 Blackwell\u003c\/td\u003e\n                        \u003ctd\u003e48GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e2377 MHz\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX PRO 4500 Blackwell\u003c\/td\u003e\n                        \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e2407 MHz\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA 6000 ADA Generation\u003c\/td\u003e\n                        \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n                        \u003ctd\u003e2505 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2500 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 5090\u003c\/td\u003e\n                        \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2407 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 5000 ADA Generation\u003c\/td\u003e\n                        \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n                        \u003ctd\u003e2550 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2250 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 4500 ADA Generation\u003c\/td\u003e\n                        \u003ctd\u003e24GB GDDR6\u003c\/td\u003e\n                        \u003ctd\u003e2580 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2250 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 4000 ADA Generation\u003c\/td\u003e\n                        \u003ctd\u003e20GB GDDR6\u003c\/td\u003e\n                        \u003ctd\u003e2175 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2250 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 5080\u003c\/td\u003e\n                        \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e1875 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2617 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 5070 Ti\u003c\/td\u003e\n                        \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2452 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 5070\u003c\/td\u003e\n                        \u003ctd\u003e12GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2512 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                    \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX 5060 Ti\u003c\/td\u003e\n                        \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n                        \u003ctd\u003e1750 MHz\u003c\/td\u003e\n                        \u003ctd\u003e2572 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX A1000\u003c\/td\u003e\n                        \u003ctd\u003e8GB GDDR6\u003c\/td\u003e\n                        \u003ctd\u003e1462 MHz\u003c\/td\u003e\n                        \u003ctd\u003e1500 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eNVIDIA RTX A400\u003c\/td\u003e\n                        \u003ctd\u003e4GB GDDR6\u003c\/td\u003e\n                        \u003ctd\u003e1762 MHz\u003c\/td\u003e\n                        \u003ctd\u003e1500 MHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                 \n                \u003c\/tbody\u003e\n            \u003c\/table\u003e\n\n            \n        \u003c\/div\u003e\n    \u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Cloud Ninjas Ghost Viper Liquid Cooled","offer_id":42231443882030,"sku":"Cloud Ninjas WIUU-3N4S-1G-LC-Intel-Edition-LocalAI","price":1109.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Define_7_Mini-WIUU-3N4S-1G-1-LQC.jpg?v=1771954936"},{"product_id":"cloud-ninjas-workstations-for-upscayl-ryzen-edition","title":"Cloud Ninjas Workstations for Upscayl Ryzen Edition","description":"\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"Product\",\n  \"@id\": \"https:\/\/cloudninjas.com\/products\/cloud-ninjas-workstations-for-upscayl-ryzen-edition#product\",\n  \"name\": \"Cloud Ninjas Workstations for Upscayl Ryzen Edition\",\n  \"description\": \"Configurable AMD Ryzen workstation designed for Upscayl AI image upscaling, image enhancement, batch processing, and other GPU-accelerated image processing workflows.\",\n  \"url\": \"https:\/\/cloudninjas.com\/products\/cloud-ninjas-workstations-for-upscayl-ryzen-edition\",\n  \"brand\": {\n    \"@type\": \"Brand\",\n    \"name\": \"Cloud Ninjas\"\n  },\n  \"category\": \"AI Image Processing Workstation\",\n  \"offers\": {\n    \"@type\": \"Offer\",\n    \"url\": \"https:\/\/cloudninjas.com\/products\/cloud-ninjas-workstations-for-upscayl-ryzen-edition\",\n    \"priceCurrency\": \"USD\",\n    \"price\": \"11503.48\",\n    \"availability\": \"https:\/\/schema.org\/InStock\",\n    \"itemCondition\": \"https:\/\/schema.org\/NewCondition\",\n    \"seller\": {\n      \"@type\": \"Organization\",\n      \"name\": \"Cloud Ninjas\",\n      \"url\": \"https:\/\/cloudninjas.com\/\"\n    }\n  }\n}\n\u003c\/script\u003e\n\n\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' Upscayl Workstation\u003c\/span\u003e\n        \u003c\/div\u003e\n\n        \u003cdiv class=\"subsection-information\"\u003e\n            \u003cp\u003eWhile Upscayl relies primarily on GPU acceleration, the CPU is responsible for managing file I\/O, preprocessing, and post-processing. Strong multi-core performance improves efficiency when handling batch jobs, while good single-core performance enhances overall system responsiveness during active 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 7 9700X\u003c\/td\u003e\n                        \u003ctd\u003e8C\/16T\u003c\/td\u003e\n                        \u003ctd\u003e3.80 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                    \u003ctr\u003e\n                        \u003ctd\u003eAMD Ryzen 7 9800X3D\u003c\/td\u003e\n                        \u003ctd\u003e8C\/16T\u003c\/td\u003e\n                        \u003ctd\u003e4.70 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.20 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                    \u003ctr\u003e\n                        \u003ctd\u003eAMD Ryzen 9 9900X\u003c\/td\u003e\n                        \u003ctd\u003e12C\/24T\u003c\/td\u003e\n                        \u003ctd\u003e4.40 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.60 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                    \u003ctr\u003e\n                        \u003ctd\u003eAMD Ryzen 9 9900X3D\u003c\/td\u003e\n                        \u003ctd\u003e12C\/24T\u003c\/td\u003e\n                        \u003ctd\u003e4.40 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.50 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eAMD Ryzen 9 9950X\u003c\/td\u003e\n                        \u003ctd\u003e16C\/32T\u003c\/td\u003e\n                        \u003ctd\u003e4.30 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.70 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                  \u003ctr\u003e\n                        \u003ctd\u003eAMD Ryzen 9 9950X3D\u003c\/td\u003e\n                        \u003ctd\u003e16C\/32T\u003c\/td\u003e\n                        \u003ctd\u003e4.30 GHz\u003c\/td\u003e\n                        \u003ctd\u003e5.70 GHz\u003c\/td\u003e\n                    \u003c\/tr\u003e\n                \u003c\/tbody\u003e\n            \u003c\/table\u003e\n\n        \u003c\/div\u003e\n    \u003c\/div\u003e\n\n    \u003cdiv class=\"subsection gpu-subsection\"\u003e\n        \u003cdiv class=\"sub-section-head\" id=\"gpu-subsection-toggle\"\u003e\n            \u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas' Upscayl Workstation\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 Upscayl, as AI-based image enhancement models run on the GPU. A workstation GPU with strong compute capability and adequate VRAM enables faster upscaling, supports higher-resolution images, and allows for smoother batch processing. Increased VRAM capacity is especially important for handling large images or multiple concurrent tasks without memory limitations, directly improving performance in professional AI-driven imaging workflows.\n\u003c\/p\u003e\n          \u003cp\u003e\n            Upscayl can process multiple images as part of a batch workflow, making it useful for users who need to enhance larger collections of photographs, artworks, or other image assets. Processing requirements will vary depending on image resolution, selected model, scaling factor, and the number of images being processed.\n          \u003c\/p\u003e\n\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 Steel Wolf Liquid Cooled","offer_id":42233192284206,"sku":"Cloud Ninjas WARU-4N4S-2G-Ryzen-Edition-Upscayl","price":1929.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0248\/8539\/5502\/files\/Cloud_Ninjas_WARU-4N4S-2G_Liquid_Cooled_1.jpg?v=1775674169"}],"url":"https:\/\/cloudninjas.com\/collections\/cloud-ninjas-workstations-for-generative-ai.oembed?page=2","provider":"Cloud Ninjas","version":"1.0","type":"link"}