Start with the training workload and the facility—not a GPU count or a vendor’s headline performance claim. The right server depends on your model, precision, sequence length, data and parallelism plan, as well as the power, cooling, storage and network capacity available where it will run. Use those requirements to compare complete, vendor-supported configurations and quotes; no single server is best for every training job.
Define the training workload before choosing hardware
Ask the people who will build and run the model to document the job before requesting server quotes. “AI training” can mean anything from fine-tuning a model on one server to pretraining a large model across multiple nodes, and those jobs can place very different demands on memory, communication and data access.
- Model and job: Record the model size and whether the work is pretraining, continued training or fine-tuning.
- Training settings: Specify precision, sequence length, batch size assumptions and the training framework or software stack.
- Data and outputs: Estimate dataset volume, staging and caching needs, checkpoint frequency and retention, and the storage path the job will use.
- Scale and schedule: Note expected concurrency, training duration, whether jobs must span nodes, and how much room the system needs for future workloads.
Have the engineering team estimate accelerator memory and communication needs for the actual workload. Aggregate GPU memory is not the same as memory available to one process, and it does not by itself establish that a model will fit or train efficiently. Usable memory and distributed-training behavior depend on the workload. NVIDIA’s reference architecture publishes platform capacities and design requirements, but does not calculate a buyer’s model fit or establish a universally sufficient GPU count. NVIDIA HGX AI Factory components
How much GPU memory and interconnect do you need?
Use GPU memory per accelerator and the GPU-to-GPU interconnect topology as workload-planning inputs. The figures below are vendor specifications for NVIDIA’s eight-GPU HGX reference platforms—not independent benchmarks, training-speed predictions or evidence that one option is more cost-effective for your job.
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| Eight-GPU HGX reference platform | Published aggregate GPU memory | Published GPU-to-GPU bandwidth |
|---|---|---|
| HGX H100 | Up to 640 GB | 900 GB/s |
| HGX H200 | Up to 1,128 GB | 900 GB/s |
| HGX B200 | Up to 1,440 GB | 1,800 GB/s |
Source for all figures in the table: NVIDIA HGX AI Factory components. NVIDIA specifies the H100/H200 and B200 baseboard bandwidths shown; the figures describe the platform design, not an application-level result.
When comparing a quote with those reference figures, confirm the exact GPU model and form factor, memory per GPU, number of GPUs, and interconnect topology in the quoted configuration. Ask the vendor or integrator to explain how that topology serves your training plan, especially if a job is distributed across GPUs or nodes. A newer generation or a larger aggregate-memory number alone does not establish that a system will meet your requirements.
Will the host, PCIe layout and storage keep the GPUs supplied?
Accelerators do not operate in isolation. Compare the CPU, system memory, PCIe connectivity, network adapters and local storage as one design, and request the topology for the exact configuration—not just a parts list. GPU links, NICs and NVMe devices need the connectivity and placement required by the platform design.
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For its eight-GPU HGX H100/H200/B200 reference system, NVIDIA specifies two CPU sockets, at least 48 physical CPU cores per socket and at least 1.5 TB of total system memory. It also calls for balanced PCIe connectivity across CPU sockets and root ports. These are requirements for that reference platform, not minimum specifications for every AI training server or a smaller workstation. NVIDIA HGX AI Factory components
Storage needs depend on what the job reads and writes, and on whether data is staged locally or accessed from shared storage. For its training and deep-learning server reference architecture, NVIDIA recommends at least 2 TB of NVMe storage per CPU socket and a 1 TB boot drive; it also notes that additional local storage may be needed for image storage. Treat those figures as a starting configuration for that architecture, not as a guarantee that a particular dataset or checkpoint workflow will be served adequately. NVIDIA HGX AI Factory components
Ask the team specifying the system to account for dataset staging, local cache, checkpoint writes, logs and images, plus the throughput and connection to shared storage. Capacity alone does not tell you whether data will arrive quickly enough for your workload.
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What networking does the training plan require?
For a single-node job, ask which communication stays on the server’s GPU interconnect and what external network traffic the job still needs. If training spans nodes, treat the fabric as part of the system purchase: the server NICs, switches, cabling, storage path and congestion behavior must be considered together.
For its eight-GPU HGX deployment guidance, NVIDIA recommends capacity equivalent to one NIC per GPU and 400 GB/s of total compute-network bandwidth; its stated minimum is greater than 200 GB/s. The guidance describes BlueField-3 SuperNICs with RDMA/RoCE acceleration and up to 400 Gb/s per adapter. These are NVIDIA recommendations and specifications for the cited platform and software stack, not universal requirements for every cluster. Note that the stated total compute-network bandwidth is in GB/s, while the per-adapter figure is in Gb/s. NVIDIA HGX AI Factory components
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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Do not count only the cluster-facing adapters. NVIDIA distinguishes East-West compute traffic between servers from North-South traffic for customer access, storage and management. Have the quote identify each network and connection your deployment needs, including how storage is attached and how administrators will manage the systems. NVIDIA HGX AI Factory components
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Can your rack, power and cooling support the server?
Get facilities approval against the exact proposed SKU and its installation documentation before ordering. Check rack units and depth, system weight, power delivery and redundancy, connectors and PDU compatibility, sustained electrical capacity, cooling and heat rejection, airflow direction, service clearances and the operating environment. A server that fits the compute plan but not the room is not a viable purchase.
NVIDIA’s DGX H100/H200 system illustrates why the facility check needs model-specific figures. NVIDIA documents this 8U system with six 3.3 kW power supplies in a 4+2 redundancy configuration, maximum system power of 10.2 kW at 200–240 V AC, heat output of 38,557 BTU/hr, and front-to-back airflow of 1,105 CFM at 80% fan PWM. Its documented operating temperature range is 5–30°C. These specifications apply to DGX H100/H200; they should not be applied to other server models or treated as figures for every operating condition. Confirm requirements for the exact server under consideration with its installation guide and the OEM. NVIDIA DGX H100/H200 system guide
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you shortlist OEM systems and compare quotes?
A vendor-validated configuration can be a useful starting point for a shortlist. NVIDIA’s Certified Systems catalog lists tested HGX configurations, including these examples:
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| Example system | HGX configurations listed |
|---|---|
| Dell PowerEdge XE9680 | H100, H200 |
| Lenovo ThinkSystem SR680a V3 | H100, H200, B200 |
| Supermicro AS-4125GS-TNHR2-LCC | H100, H200 |
Examples and listed platform pairings are from NVIDIA-Certified Systems. Certification indicates that listed configurations were tested; it does not rank vendors, establish performance for your workload, or guarantee price, support terms or availability. Verify the exact SKU and configuration with the manufacturer or reseller.
Request comparable quotes so that apparent differences reflect real configuration or service choices rather than missing components. Use this checklist with each vendor:
- Accelerators: GPU model and count, memory per GPU, and GPU-to-GPU topology.
- Host: CPU sockets and cores, total system memory, and documented PCIe lane and root-port layout.
- Network: NIC count and placement, adapters and speeds, and the switches, cabling and fabric needed for the intended cluster.
- Storage: Local NVMe capacity, boot storage, and the shared-storage connection and throughput assumptions.
- Facility: Rack footprint, weight, power and redundancy, connectors, cooling, airflow and service requirements for the exact SKU.
- Support and delivery: Validated configuration, warranty, support response terms, software support or licensing, geography and delivery schedule.
- Total cost: Complete acquisition and operating costs using current vendor quotes and your local electricity and facility rates.
The cited official materials do not establish a cross-vendor performance-per-dollar ranking or current street prices. Ask vendors to state quote assumptions clearly, and do not treat a certification, component specification or aggregate GPU-memory figure as a substitute for workload-specific evaluation.
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