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NVIDIA DGX H200

Eight H200 GPUs with 1,128GB of HBM3e — the memory-heavy Hopper DGX.

Full manufacturer warrantyAuthorized channel48-hour quote

We help you choose, configure, and deliver the right system — no obligation.

NVIDIA DGX H200 — NVIDIA enterprise hardware
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NVIDIA DGX H200 hardware detail 1
NVIDIA DGX H200 hardware detail 2
NVIDIA DGX H200 hardware detail 3

Configuration at a Glance

GPU / Accelerator8× NVIDIA H200 SXM
GPU Memory1,128GB HBM3e total (141GB per GPU)
GPU InterconnectNVLink with NVSwitch
CPUDual Intel Xeon Platinum

Tailored per engagement. Full technical overview below.

Configuration Options

Core specifications for this system. Every component is configurable to your workload — request a quote for a tailored build.

GPU / Accelerator

8× NVIDIA H200 SXM

Processor

Dual Intel Xeon Platinum

Memory

1,128GB HBM3e total (141GB per GPU)

Overview

DGX H200 keeps the Hopper-generation DGX chassis and operational model while raising GPU memory substantially: 141GB of HBM3e per GPU for 1,128GB across the node, against 640GB on DGX H100. For inference on large models that difference frequently removes GPUs from every node, which is where the economics turn. We supply it configured, with fabric and storage quoted alongside.

Who This Solution Is For

Teams serving large models where GPU memory is the binding constraint
Organisations extending an existing Hopper-generation DGX estate
Buyers who want DGX support and software with more memory per GPU
Research groups running memory-bound training workloads

Business Benefits

More memory per GPU

141GB HBM3e per GPU means larger models and longer context fit without sharding across nodes.

Same operational model

Shares the DGX H100 chassis generation, so it slots into an existing Hopper estate.

Fewer nodes for inference

Where memory is the constraint, consolidation reduces both the node count and the fabric around it.

Supported end to end

NVIDIA enterprise support across hardware and the DGX software environment.

Typical Business Use Cases

1

Large-model inference serving with long context windows

2

Memory-bound training and fine-tuning

3

Extending an existing DGX H100 cluster

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Consolidating inference capacity onto fewer nodes

Industry Applications

AI & Machine LearningResearch & Higher EducationFinancial ServicesHealthcare & Life Sciences

Technical Overview

An 8U system with eight H200 SXM GPUs on an NVLink baseboard with NVSwitch, dual Intel Xeon Platinum processors, and ConnectX-7 adapters for 400Gb/s InfiniBand. Exact memory and storage configuration is confirmed against the current NVIDIA revision at quotation.

GPU / Accelerator8× NVIDIA H200 SXM
GPU Memory1,128GB HBM3e total (141GB per GPU)
GPU InterconnectNVLink with NVSwitch
CPUDual Intel Xeon Platinum
System Memory2TB DDR5 (configuration confirmed at quote)
NetworkingConnectX-7 400Gb/s InfiniBand; BlueField-3 DPUs
Form Factor8U rackmount
SourcingConfigured and supplied by Nexus Compute; availability confirmed with quote

Specifications are indicative and configured to each engagement. Request a quote for a configuration tailored to your requirements.

Warranty, Support & Fulfillment

Every system ships from an authorized channel, configured and tested, with the documentation enterprise buyers need — backed by warranty and a dedicated account team.

Enterprise Warranty

Full manufacturer warranty with optional on-site, next-business-day support and extended coverage.

Authorized Channel

Sourced through Tier-1 distribution and OEM partners — never grey market. Asset & warranty records included.

Lead Time & Deployment

48-hour quotes, then configured, burn-in tested, and delivered on a committed schedule.

Nationwide Fulfillment

Coordinated logistics, rack-and-stack, and delivery wherever your infrastructure lives.

Frequently Asked Questions

Is DGX H200 worth it over DGX H100?

It depends entirely on whether GPU memory is what limits you. The compute is Hopper-generation in both. If your models and KV cache already fit comfortably on H100, the extra memory buys headroom you may not use. If you are sharding across GPUs purely to fit, H200 can consolidate that and reduce your node count. Send us the model sizes and context length and we will quote both.

How is DGX quoted and delivered?

As a configured system rather than a boxed part. We confirm the exact platform revision, the networking and storage around it, rack power and cooling requirements, and the support term, then issue an itemised quote. Current availability and lead time are stated on that quote — accelerator allocation moves, and any figure published on a web page would be wrong for most buyers by the time they read it.

Should I buy DGX or an HGX system from Dell, Supermicro or HPE?

The GPUs and the NVLink fabric are the same silicon either way. DGX gives you one vendor accountable for the whole stack including the software environment, and is the designed unit if you are heading toward SuperPOD. An HGX system from a server vendor gives you your existing management tooling, support relationship and configuration choices. If your operations are already standardised on Dell or HPE, that consistency is frequently worth more than it looks on a spreadsheet. We supply both and will tell you which fits.

Can you supply the networking and storage as well?

Yes, and these should be quoted together. A DGX node without its InfiniBand fabric, parallel storage and rack power is not a working cluster. We specify the switches, cables, optics and layout alongside the systems.

Hardware Assistance

Configure the NVIDIA DGX H200 with Nexus Compute

Tell us your requirements and a hardware specialist will help you specify, configure, and quote the right system — typically within two business days. No obligation.