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NVIDIA HGX H200 SXM eight-GPU reference platform
NVIDIA HGX H200 reference showing eight SXM GPU modules installed on the baseboard. This platform image illustrates the SXM form factor; this listing describes one H200 SXM GPU, not the entire eight-GPU board. Image source ↗

NVIDIA · Integrated GPU platform / module

NVIDIA H200 SXM

US$30,800.00 Per unit International supply by quotation
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Priced item: NVIDIA H200 SXM5 141GB HBM3e Tensor Core GPU module; one module for HGX H200 baseboard

Seller SKU GW-NVIDIA-H200-SXM

Supplied unit: One H200 SXM5 141GB GPU module for a supported HGX H200 baseboard. The baseboard and complete server are separate.

Your written quotation confirms the USD price, shipping, any destination taxes or duties, warranty and payment terms.

Product overview

NVIDIA H200 SXM5 141GB HBM3e Tensor Core GPU module; one module for HGX H200 baseboard. Supplied unit: One H200 SXM5 141GB GPU module for a supported HGX H200 baseboard. The baseboard and complete server are separate. NVIDIA model-level reference specifications — reference memory / capacity: 141 GB HBM3e; interface: SXM / NVLink; host PCIe Gen5 interconnect; form factor: SXM GPU module. Confirm the exact OEM configuration and its host, power, cooling and interconnect requirements before ordering.

Configuration overview

This is an integrated GPU module or system platform with host-specific requirements. Confirm the exact module or complete-system scope, interconnect, power, cooling and supported software in your quotation.

Specifications & compatibility

NVIDIA REFERENCE SPECIFICATIONS

Technical specifications

Specifications describe the identified NVIDIA model or reference configuration. Board-partner designs, OEM firmware, laptop power limits and complete-system configurations can differ.

GPU architecture
Hopper
GPU memory per GPU
141 GB HBM3e
Memory bandwidth per GPU
4.8 TB/s
FP64 peak per GPU
34 TFLOPS
FP64 Tensor peak per GPU
67 TFLOPS
FP32 peak per GPU
67 TFLOPS
TF32 Tensor peak per GPU, sparse
989 TFLOPS with structured sparsity
FP16 / BF16 Tensor peak per GPU, sparse
1,979 TFLOPS with structured sparsity
FP8 Tensor peak per GPU, sparse
3,958 TFLOPS with structured sparsity
TDP per GPU
Up to 700 W, configurable
NVLink bandwidth per GPU
900 GB/s
PCIe system interconnect
PCIe Gen5; 128 GB/s bidirectional
Form factor
SXM module; requires compatible HGX/DGX system
Video decode engines
7 NVDEC and 7 JPEG decoders
MIG partitioning
Up to 7 isolated GPU instances

All capacities, TDPs and arithmetic rates above are per GPU, not per NVLink pair or complete server. Tensor figures explicitly marked sparse require supported structured sparsity.

Capacity / memory
141 GB HBM3e
Interface
SXM / NVLink; host PCIe Gen5 interconnect
Form factor
SXM GPU module

An integrated platform or module. Confirm its supported host, interconnect, power and cooling configuration.

SXM is an integrated GPU module requiring a supported HGX/OEM system, not a standalone PCIe card.

Listed official product; exact supplied platform and condition by quote

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Match the part to its host.

NVIDIA model names identify GPUs and product editions; they do not establish one universal board-vendor part number. Laptop GPUs are supplied within complete laptops. SXM modules, HGX/DGX systems and integrated CPU–GPU platforms require their supported host and interconnect.

Memory type, rank, transfer rate and population rules must match the server and CPU. A storage capacity does not establish NVMe, SATA or SAS compatibility. SSD and mechanical HDD media, firmware, sector format, carriers and security variants need separate checks.

For a precise quotation, send:

  1. Complete manufacturer/OEM part number and label, including suffix.
  2. Quantity, acceptable condition and delivery city/country.
  3. Server, motherboard, CPU or storage-backplane model.
  4. Application, required configuration and compatibility requirements.

Where a price is not displayed, call or WhatsApp for the current USD price. Confirm equipment condition, quantity, warranty, shipping and dispatch before ordering.

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