NVIDIA H200 NVL 141GB PCIE GPU Accelerator (número de peça 900-21010-0040-000) para AI e HPC generativos
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  • Número da peça:NVIDIA H200 NVL 141GB
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Title

NVIDIA H200 NVL 141GB PCIe GPU Accelerator (Part Number 900-21010-0040-000) for Generative AI & HPC

Keywords

NVIDIA H200 NVL, 141GB HBM3e memory, PCIe Gen5 x16 accelerator, AI inference GPU, Hopper architecture GPU, buy H200 NVL, enterprise GPU accelerator, 900-21010-0040-000

Description

The NVIDIA H200 NVL (PN 900-21010-0040-000) is a premium GPU accelerator card built on NVIDIA’s Hopper architecture. It features **141 GB of HBM3e** ultra-fast memory with **4.8 TB/s memory bandwidth**, delivering exceptional performance for generative AI inference, large language models (LLMs), and high-performance computing (HPC) workloads.

The PCIe Gen5 ×16 interface allows this accelerator to be deployed in a wide range of servers that support modern PCIe slots, providing high throughput and compatibility. With part number 900-21010-0040-000, this unit is ideal for OEM integrators or existing GPU server upgrades. 

Its design supports multiple instances per GPU via NVIDIA’s MIG-style partitioning, enabling flexible usage for multi-tenant inference, virtualized deployment, or fine-tuning smaller models. The higher memory and bandwidth make it well-suited to handle larger model weights and data without frequent data swaps.

Power requirements are significant: TDP can go up to ~600–700W depending on configuration and cooling. Ensure that server power delivery, cooling, and form factor can support this module. 

For enterprises planning to buy H200 NVL, this card represents a major leap forward over previous-generation GPUs in memory capacity, inference throughput, and overall efficiency for AI and HPC tasks. It’s especially valuable for inference farms, model serving, large-scale data science, or multi-GPU cluster use.

Key Features

  • 141 GB of HBM3e GPU memory with **4.8 TB/s memory bandwidth** for high throughput. 141GB HBM3e memory 
  • Supports PCIe Gen5 ×16 form factor for broad compatibility. PCIe Gen5 x16 accelerator 
  • High precision and mixed precision support: FP64, FP32, TF32, BFLOAT16, FP16, INT8, FP8, with Tensor Cores to accelerate deep learning and scientific computing. 
  • Multi-Instance GPU (MIG-style) support for partitioning resources per task or user, enabling flexible usage.
  • High interconnect options: NVLink or NVLink bridge in multi-GPU setups to allow very high bandwidth between GPUs. 
  • Passive or air-cooled PCIe form (in NVL version) enabling deployment in standard server racks without liquid cooling for certain configurations. 

Configuration

ComponentSpecification / Details
Part Number / ModelNVIDIA H200 NVL – PN 900-21010-0040-000
GPU Memory141 GB HBM3e
Memory Bandwidth≈ 4.8 TB/s
InterfacePCI Express Gen5 ×16 (NVL passive PCIe form)
Precision & ComputeFP64, FP32, TF32, BFLOAT16, FP16, INT8, FP8; Tensor Cores included. 
Multi-Instance SupportUp to 7 MIGs / instances depending on configuration. 
Power ConsumptionUp to ~600–700 W depending on workload / cooling. 
Cooling & Form FactorPassive/air-cooled PCIe form (NVL version) or SXM boards in some systems with NVLink. 
Interconnect / BridgesNVLink bridges for multi-GPU, PCIe Gen5 for host I/O. 
Supported Software / StackNVIDIA AI Enterprise, CUDA, cuDNN, TensorRT, ONNX, etc. 

Compatibility

The NVIDIA H200 NVL requires a server with a free PCIe Gen5 x16 slot and sufficient power delivery capacity (600-700W headroom), robust cooling (air flow, fan capacity), and a compatible BIOS / firmware that supports recent NVIDIA drivers. 

It is supported in many NVIDIA-Certified systems, including those from Lenovo ThinkSystem, Dell, and other OEMs that list H200 NVL as a certified option. LGX / MGX / HGX NVLink options exist where NVLink bridges are used for multi-GPU setups.

Software compatibility includes Linux distributions like Ubuntu 24.04 LTS, Red Hat Enterprise Linux 9.x, and others; drivers via NVIDIA CUDA / AI Enterprise stack. Verify that your kernel and OS support the Hopper architecture and necessary driver versions. 

Usage Scenarios

1) Large Language Model (LLM) Inference & Serving: With 141 GB memory and high bandwidth, the H200 NVL can serve large-parameter models with fewer memory bottlenecks, enabling faster inference and reduced context-switch overhead. Ideal for AI-as-a-service or APIs.

2) High-Performance Computing (HPC) and Scientific Simulations: Workloads involving FP64, large matrix operations, computational fluid dynamics, and climate modeling will benefit from the Hopper architecture and HBM3e bandwidth.

3) AI Training (Fine-Tuning) & Mixed Precision Workloads: While full training of massive models might use SXM versions, the NVL form is excellent for fine-tuning or mixed precision training, and offers good memory bandwidth and size.

4) Data Center GPU Cloud Infrastructure: In multi-tenant GPU clouds, the ability to partition the H200 into multiple instances (MIG/other partitioning) helps allocate GPU resources efficiently. Also useful for GPU aggregation nodes.

5) Graphics / Rendering / Visualization: For high resolution rendering, 3D simulation, VR/AR content, scientific visualization, the large memory buffer and high bandwidth can hold large datasets and texture assets.

Frequently Asked Questions

  1. Q: What is the difference between H200 NVL and H200 SXM / HGX versions?
    A: The NVL version is a PCIe form-factor variant; it tends to have passive or air cooling and is designed for broader server compatibility via PCIe Gen5. SXM/HGX versions offer interconnects like NVLink/HGX boards, higher power envelope and often better cooling and multi-GPU scaling. 
  2. Q: How many GPU partitions (MIG instances) can I run on the H200 NVL?
    A: Up to **7 MIG-style instances** are supported depending on configuration; each instance gets ~16.5 GB memory in some partitioning modes. This enables multi-tenant or multi-workload deployment. 
  3. Q: What server power supply & cooling should I plan for when installing this GPU?
    A: Plan for at least **600-700W** headroom for this GPU under load, plus sufficient cooling in the chassis. Ensure PCIe slot has proper support, airflow is unobstructed, and power connectors meet NVIDIA specifications.
  4. Q: Will the GPU memory be fully usable for large model inference without offloading?
    A: Yes—the 141 GB HBM3e provides a large contiguous memory pool, reducing need for offloading or frequent data movement. But actual usable capacity depends on model size, precision, batch size, and any memory reserved by the system or driver overhead. Mixed precision (FP16/FP8) can also help fit larger models.
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