{"product_id":"wiw35u-5n6s-4g-ai-workstation","title":"WIW35U-5N6S-4G AI Workstation","description":"\u003cdiv class=\"subsection cpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"cpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eProcessor Specifications for Cloud Ninjas WIW35U-5N6S-4G\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \u003cp\u003eCloud Ninjas WIW35U-5N6S-4G focus in offering the best Intel Xeon W series processors together with the best possible GPUs for heavy AI\/ML workloads on a workstation form factor. With 3 NVMe M.2, 2 NVMe U.2 and 2 SATA solid state drives, the WIW35U-5N6S-4G is the perfect solution for a large form factor system that runs any AI Model, heavy 3D modeling, video editing, and other advanced workstation workloads. Built for users who need high performance workstations in a workstation form factor on premise, rather than stored at data centers. \u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n \u003cdiv class=\"table-section\"\u003e\n \u003cdiv class=\"cpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eCPU\u003c\/th\u003e\n \u003cth\u003eCores \u0026amp; Threads\u003c\/th\u003e\n \u003cth\u003eBase Clock\u003c\/th\u003e\n \u003cth\u003eTurbo Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Xeon w5-3525\u003c\/td\u003e\n \u003ctd\u003e16C\/32T\u003c\/td\u003e\n \u003ctd\u003e3.20 GHz\u003c\/td\u003e\n \u003ctd\u003e4.80 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Xeon w5-3535X\u003c\/td\u003e\n \u003ctd\u003e20C\/40T\u003c\/td\u003e\n \u003ctd\u003e2.90 GHz\u003c\/td\u003e\n \u003ctd\u003e4.80 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Xeon w7-3545\u003c\/td\u003e\n \u003ctd\u003e24C\/48T\u003c\/td\u003e\n \u003ctd\u003e2.70 GHz\u003c\/td\u003e\n \u003ctd\u003e4.80 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Xeon w7-3555\u003c\/td\u003e\n \u003ctd\u003e28C\/56T\u003c\/td\u003e\n \u003ctd\u003e2.70 GHz\u003c\/td\u003e\n \u003ctd\u003e4.80 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Xeon w7-3565X\u003c\/td\u003e\n \u003ctd\u003e32C\/64T\u003c\/td\u003e\n \u003ctd\u003e2.50 GHz\u003c\/td\u003e\n \u003ctd\u003e4.80 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Xeon w9-3575X\u003c\/td\u003e\n \u003ctd\u003e44C\/88T\u003c\/td\u003e\n \u003ctd\u003e2.20 GHz\u003c\/td\u003e\n \u003ctd\u003e4.80 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIntel Xeon w9-3595X\u003c\/td\u003e\n \u003ctd\u003e60C\/120T\u003c\/td\u003e\n \u003ctd\u003e2.00 GHz\u003c\/td\u003e\n \u003ctd\u003e4.80 GHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \u003c\/div\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection gpu-subsection\"\u003e\n \u003cdiv class=\"sub-section-head\" id=\"gpu-subsection-toggle\"\u003e\n \u003cspan class=\"sub-section-tl\"\u003eGraphics Card Specifications for Cloud Ninjas WIW35U-5N6S-4G\u003c\/span\u003e\n \u003c\/div\u003e\n\n \u003cdiv class=\"subsection-information\"\u003e\n \t\u003cp\u003eIn machine learning, AI, video editing, 3D Modeling among other heavy workloads, the GPU remains the most critical performance component, as modern workloads such as model training, fine-tuning and inference rely on GPU accelaration. A multiple high-performance consumer or enterprise GPU infrastructure with ample VRAM enables high efficiency processing of large datasets, execution of complex models, and consistent performance across development and production workflows. High speeds for video editing, and 3D modeling renderization with high VRAM GPUs can be achieved at the highest performance. For AI and machine learning, higher VRAM capacity allows models, data and tensors to remain in the GPU, reducing memory transfers and latency to improve overall performance in matrix and neural network computations, hence the Cloud Ninjas WIW35U-5N6S-4G, offers the best non data-center platform for AI workloads, by offering up to 4 consumer or enterprise GPUs.\n\u003c\/p\u003e\n\n\u003cp\u003eFor space-constrained and edge-focused AI infrastructure, carefully selecting a Multi-GPU configuration can deliver extreme top performance, and system-level optimization. This system values speed and performance computation through proper cooling solutions, reliable power delivery, and system-level optimization. In enterprise and professional environments, consistent performance over long time periods is essential. And the Cloud Ninjas WIW35U-5N6S-4G achieves just that perfectly. \u003c\/p\u003e\n \u003c\/div\u003e\n\n \u003c\/div\u003e\n\n \u003cdiv class=\"table-section\"\u003e\n\n \u003cdiv class=\"gpu-compatibility-table\"\u003e\n \u003ctable class=\"data-table\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003eGPU\u003c\/th\u003e\n \u003cth\u003eVRAM\u003c\/th\u003e\n \u003cth\u003eGPU Clock\u003c\/th\u003e\n \u003cth\u003eMemory Clock\u003c\/th\u003e\n \u003c\/tr\u003e\n \u003c\/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 6000 Blackwell Max Q Workstation Edition\u003c\/td\u003e\n \u003ctd\u003e96GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2280 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 5000 Blackwell\u003c\/td\u003e\n \u003ctd\u003e48GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2377 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX PRO 4500 Blackwell\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA 6000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2505 MHz\u003c\/td\u003e\n \u003ctd\u003e2500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5090\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2407 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e32GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2550 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4500 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e24GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2580 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 4000 ADA Generation\u003c\/td\u003e\n \u003ctd\u003e20GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e2175 MHz\u003c\/td\u003e\n \u003ctd\u003e2250 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5080\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1875 MHz\u003c\/td\u003e\n \u003ctd\u003e2617 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2452 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5070\u003c\/td\u003e\n \u003ctd\u003e12GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2512 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX 5060 Ti\u003c\/td\u003e\n \u003ctd\u003e16GB GDDR7\u003c\/td\u003e\n \u003ctd\u003e1750 MHz\u003c\/td\u003e\n \u003ctd\u003e2572 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A1000\u003c\/td\u003e\n \u003ctd\u003e8GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1462 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eNVIDIA RTX A400\u003c\/td\u003e\n \u003ctd\u003e4GB GDDR6\u003c\/td\u003e\n \u003ctd\u003e1762 MHz\u003c\/td\u003e\n \u003ctd\u003e1500 MHz\u003c\/td\u003e\n \u003c\/tr\u003e\n \n \u003c\/tbody\u003e\n \u003c\/table\u003e\n\n \n \u003c\/div\u003e\n \u003c\/div\u003e","brand":"Cloud Ninjas","offers":[{"title":"Default Title","offer_id":48026185007321,"sku":"CN-WIW35U-5N6S-4G","price":3203.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0016\/5327\/6707\/files\/Define_7_XL-WIW35U-5N6S-4G-1.jpg?v=1772021178","url":"https:\/\/www.aloinfo.com.co\/products\/wiw35u-5n6s-4g-ai-workstation","provider":"aloinfousa.com","version":"1.0","type":"link"}