{"id":8985,"date":"2026-08-27T15:35:05","date_gmt":"2026-08-27T12:35:05","guid":{"rendered":"https:\/\/unihost.com\/blog\/?p=8985"},"modified":"2026-08-27T15:38:44","modified_gmt":"2026-08-27T12:38:44","slug":"8x-h100-platform-for-large-scale-model-training","status":"publish","type":"post","link":"https:\/\/unihost.com\/blog\/8x-h100-platform-for-large-scale-model-training\/","title":{"rendered":"How Asteria AI Built an 8x H100 Platform for Large-Scale Model Training"},"content":{"rendered":"

About the client<\/span><\/h3>\n

Asteria AI develops enterprise generative AI solutions for document intelligence, knowledge management, and domain-specific language models.<\/span><\/p>\n

The company works with large proprietary datasets and builds customized models for customers in financial services, legal technology, and enterprise software. <\/span>Its engineering team performs model fine-tuning, continuous evaluation, large-scale inference, and experimentation with open-weight foundation models. <\/span>As model sizes and datasets increased, GPU infrastructure became one of the company’s most important technical and financial resources.<\/span><\/p>\n

Challenge<\/span><\/h3>\n

During its early development stage, Asteria AI relied primarily on public cloud GPU instances. <\/span>This approach was flexible for experiments. Engineers could allocate GPUs for several hours or days, complete a training job, and terminate the environment. <\/span>But the workload changed as the company grew, GPU utilization became almost continuous.<\/span><\/p>\n

The team was now running:<\/span><\/p>\n

    \n
  • distributed model training;<\/span><\/li>\n
  • fine-tuning of large language models;<\/span><\/li>\n
  • synthetic-data generation;<\/span><\/li>\n
  • embedding pipelines;<\/span><\/li>\n
  • evaluation workloads;<\/span><\/li>\n
  • production batch inference;<\/span><\/li>\n
  • customer-specific model experiments.<\/span><\/li>\n<\/ul>\n

    Large training jobs increasingly required 8 H100-class GPUs simultaneously, and obtaining identical GPU capacity at the required time was not always predictable. <\/span>The company also found that running large GPU instances continuously changed the economics of the public cloud. <\/span>Its requirements were therefore very different from those of an early-stage experimental AI project:<\/span><\/p>\n

      \n
    • 8 high-end GPUs in a single server;<\/span><\/li>\n
    • high-speed inter-GPU communication;<\/span><\/li>\n
    • hundreds of gigabytes of GPU memory;<\/span><\/li>\n
    • large system RAM capacity;<\/span><\/li>\n
    • fast local storage;<\/span><\/li>\n
    • high-speed networking;<\/span><\/li>\n
    • guaranteed GPU availability;<\/span><\/li>\n
    • predictable monthly infrastructure cost.<\/span><\/li>\n<\/ul>\n

      Solution<\/span><\/h3>\n

      Asteria AI moved its baseline training and inference capacity to a dedicated 8x NVIDIA H100 server with Unihost in the Netherlands. <\/span>The company selected the <\/span>AZM-12<\/b>, a large multi-GPU configuration designed for demanding AI workloads. <\/span>Unlike the temporary cloud instances previously used by the team, the entire GPU platform is dedicated to Asteria AI and remains permanently available for its ML engineers and production workloads.<\/span><\/p>\n

      Unihost solution used<\/b><\/p>\n

      AZM-12 Multi-GPU Dedicated Server in Netherlands<\/span><\/p>\n

        \n
      • 2x Intel Xeon Gold 6448Y<\/span><\/li>\n
      • 8x NVIDIA H100 NVLink<\/span><\/li>\n
      • 2 TB RAM, 2 TB NVMe<\/span><\/li>\n
      • 10 Gbps network<\/span><\/li>\n<\/ul>\n

        Used for: distributed LLM training, fine-tuning, reinforcement and preference optimization workloads, large-scale inference, synthetic-data generation, embeddings, and model evaluation. <\/span>The 8x NVIDIA H100 NVLink configuration allows Asteria AI to distribute large training workloads across multiple GPUs within a single physical machine while maintaining fast communication between accelerators. <\/span>The server’s 2 TB of system memory provides substantial capacity for preprocessing and large datasets, while the 10 Gbps connection is used for transferring datasets, model checkpoints, and results to the company’s storage environment.<\/span><\/p>\n

        Results<\/span><\/h3>\n

        Moving to dedicated H100 infrastructure changed how the engineering team used GPU resources. <\/span>Previously, engineers planned experiments around cloud GPU availability and frequently terminated environments immediately after jobs were completed.<\/span><\/p>\n

        With the dedicated 8-GPU node, the team can maintain a permanent training environment and keep datasets, Docker images, model caches, and development tooling ready for the next workload. <\/span>Average GPU utilization increased from approximately 35 – 45% to more than 70% because unused capacity could immediately be reassigned between training, evaluation, inference, and research jobs. <\/span>Asteria AI also reduced the time required to start large training workloads. <\/span>Provisioning an 8-GPU environment, synchronizing datasets, installing dependencies, and restoring checkpoints previously took anywhere from tens of minutes to several hours depending on cloud capacity and dataset size.<\/span><\/p>\n

        With a persistent dedicated environment, new workloads can begin almost immediately when GPU resources become available.<\/span><\/p>\n

        Training larger models<\/span><\/h3>\n

        The biggest architectural change was the ability to treat eight H100 GPUs as a permanent compute pool.<\/span><\/p>\n

        Asteria AI uses the server for distributed training and fine-tuning of models that would be impractical to run on one or two GPUs.<\/span><\/p>\n

        The environment supports several workload types:<\/span><\/p>\n

        Large training jobs<\/span><\/p>\n

        \u2192 all 8x H100 GPUs assigned to one distributed workload<\/span><\/p>\n

        Model fine-tuning<\/span><\/p>\n

        \u2192 4-8 GPUs depending on model size<\/span><\/p>\n

        Evaluation and inference<\/span><\/p>\n

        \u2192 GPUs divided between multiple jobs<\/span><\/p>\n

        Development and experimentation<\/span><\/p>\n

        \u2192 available capacity allocated dynamically to engineering teams<\/span><\/p>\n

        This allows the company to keep expensive GPU hardware productive even when a large training run is not active.<\/span><\/p>\n

        Predictable AI infrastructure costs<\/span><\/h3>\n

        Cost predictability was another major benefit. <\/span>Cloud GPU pricing worked well when the team used accelerators intermittently. It became less attractive once H100 workloads were running continuously. <\/span>With dedicated hardware, Asteria AI knows the cost of its baseline GPU capacity in advance and can optimize utilization around a fixed infrastructure budget. <\/span>The company still uses public cloud GPU instances when it needs to temporarily exceed the capacity of its dedicated environment. <\/span>The resulting architecture is therefore hybrid:<\/span><\/p>\n

        Unihost 8\u00d7 H100 server<\/span><\/p>\n

        \u2192 permanent training capacity<\/span><\/p>\n

        \u2192 continuous inference workloads<\/span><\/p>\n

        \u2192 fine-tuning<\/span><\/p>\n

        \u2192 evaluation<\/span><\/p>\n

        \u2192 development<\/span><\/p>\n

        Public cloud GPU capacity<\/span><\/p>\n

        \u2192 temporary large-scale experiments<\/span><\/p>\n

        \u2192 exceptional demand<\/span><\/p>\n

        \u2192 additional GPUs during major training cycles<\/span><\/p>\n

        \u201cOnce eight GPUs are busy most of the week, GPU infrastructure stops being something you provision for individual experiments. It becomes core infrastructure. Having a dedicated H100 node means our researchers can schedule workloads around available GPUs instead of scheduling their work around cloud capacity.\u201d<\/span><\/i><\/p>\n

        Operational impact<\/span><\/h3>\n

        The dedicated environment also simplified MLOps.<\/span><\/p>\n

        Instead of recreating temporary environments for each major workload, the team maintains one standardized GPU platform with:<\/span><\/p>\n

          \n
        • CUDA and NVIDIA drivers;<\/span><\/li>\n
        • PyTorch;<\/span><\/li>\n
        • distributed training frameworks;<\/span><\/li>\n
        • containerized ML environments;<\/span><\/li>\n
        • local model and dataset caches;<\/span><\/li>\n
        • monitoring;<\/span><\/li>\n
        • centralized job scheduling.<\/span><\/li>\n<\/ul>\n

          This reduced setup overhead and made experiments easier to reproduce across the engineering team.<\/span><\/p>\n

          What’s next<\/span><\/h3>\n

          Asteria AI expects its GPU requirements to continue growing as it works with larger models and larger customer datasets. <\/span>The company is evaluating a second multi-GPU server to separate production inference from research and training. <\/span>Longer term, the architecture can evolve from a single 8x H100 node into a multi-node GPU cluster, allowing the company to increase compute capacity without changing its existing ML workflow.<\/span><\/p>\n

          Results at a glance: 8x NVIDIA H100 NVLink, 70%+ GPU utilization, permanent large-model training environment, faster job startup, predictable baseline GPU capacity.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

          About the client Asteria AI develops enterprise generative AI solutions for document intelligence, knowledge management, and domain-specific language models. The company works with large proprietary datasets and builds customized models for customers in financial services, legal technology, and enterprise software. Its engineering team performs model fine-tuning, continuous evaluation, large-scale inference, and experimentation with open-weight foundation […]<\/p>\n","protected":false},"author":7,"featured_media":8986,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[46,49],"tags":[],"class_list":["post-8985","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-case-study","has-post-title","has-post-date","has-post-category","has-post-tag","has-post-comment","has-post-author",""],"yoast_head":"\nHow Asteria AI Built an 8x H100 Platform for Large-Scale Model Training - Unihost.com Blog<\/title>\n<meta name=\"description\" content=\"Asteria AI moved its baseline training and inference capacity to a dedicated 8x NVIDIA H100 server with Unihost in the Netherlands.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/unihost.com\/blog\/8x-h100-platform-for-large-scale-model-training\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How Asteria AI Built an 8x H100 Platform for Large-Scale Model Training - 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