{"id":108,"date":"2025-07-16T11:03:41","date_gmt":"2025-07-16T11:03:41","guid":{"rendered":"https:\/\/vespertec.com\/nvidia\/?page_id=108"},"modified":"2025-12-05T09:25:39","modified_gmt":"2025-12-05T09:25:39","slug":"compute","status":"publish","type":"page","link":"https:\/\/vespertec.com\/nvidia\/compute\/","title":{"rendered":"Compute"},"content":{"rendered":"
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\nAI Factories\n<\/div>

\nCompute:
The Engine Room of
Your AI Factory\n<\/h1>
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\nOverview\n<\/h1>\n<\/div>
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\nFrom desktop NVIDIA DGX\u2122 Spark systems to NVIDIA DGX SuperPOD\u2122-scale deployments, we help build the engine room of your AI factory. As an NVIDIA Elite Partner for Compute, we deliver hands-on, open infrastructure expertise: helping you choose, configure, and deploy the right system for your workloads, from the first GPU to a full \nproduction cluster.\n\n<\/div>\n<\/div><\/div><\/div>\n<\/section>
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When building any AI factory, the use case determines everything.<\/strong><\/p>\n

The type and volume of compute you need for each build depends on a few key questions, including: _<\/p>\n

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  • What applications are you running?<\/li>\n
  • What\u2019s the model size or required token rate?<\/li>\n
  • Is the app sensitive to memory latency?<\/li>\n
  • Can it run on Arm-based architectures?<\/li>\n
  • Are you training, fine-tuning, or running inference? Do you plan to support agentic AI systems?<\/li>\n
  • Do you need to support multiple users or departments with different access levels? What controls are needed to ensure security and sovereignty?<\/li>\n
  • What are your constraints on power, cooling, or rack depth?<\/li>\n<\/ul>\n

    Answering these questions helps determine whether you need a standalone single-GPU system, a 6-node BasePOD, or something in between. For example, at the edge, smaller deployments often start with short-depth MGX servers with 1-2 GPUs. Teams working on diagnostic imaging with larger user groups might prioritise GPU-to-GPU communication through NVLink or Superchip architectures. Meanwhile, a cloud service provider might prioritise a larger-scale SuperPOD or NVL72 system.<\/p>\n<\/div>\n<\/div>\n<\/div>

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    \nPractical
    Considerations\n<\/h2>\n<\/div>\n<\/div>\n<\/div><\/div><\/div>\n<\/section>
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    \nWe can help you design all of this before a single system is deployed by balancing workloads, physical constraints, and total cost of ownership.\n<\/div>\n<\/div><\/div><\/div>\n<\/section>
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    \nLiquid Cooling\n<\/h2>\n<\/div>\n<\/div>\n<\/div>
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    With server rack densities on the rise, and a single HGX system peaking at over 10kW, cooling requirements are also increasing.<\/strong><\/p>\n

    Modern GPUs are pushing beyond what fans and air cooling alone can handle efficiently. That\u2019s why more teams are exploring direct liquid cooling (DLC), direct-to-chip (D2C), and, for the right architecture, immersion.<\/strong><\/p>\n

    DLC and immersion cooling will shape how you plan your racks, your pipework, and your overall footprint for the next 5 to 10 years, so it makes sense to factor them in early. We\u2019re already working with OEMs like Supermicro, Gigabyte, and Lenovo who are shipping liquid cooling-ready systems for AI factories today.<\/p>\n<\/div>\n<\/div>\n<\/div><\/div><\/div>\n<\/section>

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    \nCompute-
    Intensive Use Cases\n<\/h2>

    \nHere are some of the
    most common applications
    driving demand for
    AI adoption today\n<\/h2>\n<\/div>
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    \nResources\n<\/h4>\n<\/div>
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    \nPractical guidance to help you accelerate performance, simplify operations, scale efficiently, and optimise total cost of ownership from your compute:\n<\/div>\n<\/div><\/div>
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