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NVIDIA H200 NVL (Tensor Core GPU 141GB) NVIDIA H200 NVL is ideal for lower-power, air-cooled enterprise rack designs and offers up to 1.7x faster large language model inference and 1.3x more performance on high-performance computing applications. Supermicro Product ID: GPU-NVH200NVL.
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NVIDIA

H200 NVL (Tensor Core GPU 141GB)

NVIDIA H200 NVL is ideal for lower-power, air-cooled enterprise rack designs and offers up to 1.7x faster large language model inference and 1.3x more performance on high-performance computing applications. Supermicro Product ID: GPU-NVH200NVL.
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29950.00
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NVIDIA ID: 900-21010-0040-000

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Specifications
H200 SXM¹ H200 NVL¹
FP64 34 TFLOPS 30 TFLOPS
FP64 Tensor Core 67 TFLOPS 60 TFLOPS
FP32 67 TFLOPS 60 TFLOPS
TF32 Tensor Core² 989 TFLOPS 835 TFLOPS
BFLOAT16 Tensor Core² 1,979 TFLOPS 1,671 TFLOPS
FP16 Tensor Core² 1,979 TFLOPS 1,671 TFLOPS
FP8 Tensor Core² 3,958 TFLOPS 3,341 TFLOPS
INT8 Tensor Core² 3,958 TFLOPS 3,341 TFLOPS
GPU Memory 141GB 141GB
GPU Memory Bandwidth 4.8TB/s 4.8TB/s
Decoders 7 NVDEC
7 JPEG
7 NVDEC
7 JPEG
Confidential Computing Supported Supported
Max Thermal Design Power (TDP) Up to 700W (configurable) Up to 600W (configurable)
Multi-Instance GPUs Up to 7 MIGs @18GB each Up to 7 MIGs @16.5GB each
Form Factor SXM PCIe
Dual-slot air-cooled
Interconnect NVIDIA NVLink™: 900GB/s
PCIe Gen5: 128GB/s
2- or 4-way NVIDIA NVLink bridge:
900GB/s per GPU
PCIe Gen5: 128GB/s
Server Options NVIDIA HGX™ H200 partner and NVIDIA-Certified Systems™ with 4 or 8 GPUs NVIDIA MGX™ H200 NVL partner and NVIDIA-Certified Systems with up to 8 GPUs
NVIDIA AI Enterprise Add-on Included

1) Preliminary specifications. May be subject to change.
2) With sparsity.

Datasheet Download datasheet
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GPU Computing

Achieve Computational Breakthroughs Through Our GPU Solutions

GPU computing utilizes graphics processing units for more than just rendering visuals, capitalizing on their ability to execute multiple tasks in parallel. Boasting thousands of cores, perfectly suited for handling complex, large-scale data sets and repetitive tasks efficiently.

This parallel processing capability is key for a broad spectrum of applications, from scientific simulations and data analysis to machine learning and graphics design. By working in tandem with CPUs, where the GPU takes on the heavy lifting for compute-intensive tasks, processing speeds are greatly enhanced.

Such a collaborative approach has cemented GPU computing as an essential component of high-performance computing (HPC) environments, particularly for powering AI-driven tasks and analyses in various fields. This synergy not only speeds up computations but also expands the potential for groundbreaking discoveries and innovations across industries.

NVIDIA GPU A100

The NVIDIA A100 80GB Graphic Card is a highly advanced GPU designed for the most demanding computational tasks.

Feature Specification
CUDA Cores 6912
Memory 80 GB HBM2e
Memory Bus Width 5120 bit
Multi-GPU Technology NVLink
API Supported OpenCL, OpenACC, DirectCompute
Number of GPUs 7
Host Interface PCI Express 4.0 x16
Power Supply Wattage 300 W
Power Connector 1x 8-pin
Cooler Type Passive Cooler
Form Factor Plug-in Card
Platform Supported PC, Linux
Environmental Certification RoHS
MPN 900-21001-0020-100

Collaboration GPU & CPU

In GPU computing, the CPU oversees the program while offloading tasks to the GPU that are suited for parallel processing, such as:

  • Complex mathematical computations and numerical simulations

  • Advanced image and video processing tasks

  • Extensive data analysis involving large datasets

The GPU steps in to manage specific operations, distributing them across multiple cores for simultaneous execution, thus enhancing overall processing efficiency.

A quick look at Supermicro’s X13 generation of GPU servers

Get a quick look at Supermicro's X13 generation GPU system. Supermicro's X13 portfolio features more than 15 system families optimized for tomorrow's data center workloads.

7 Benefits of GPU Computing

GPU computing delivers key advantages across various sectors, highlighted as follows:

With thousands of cores, GPUs excel at parallel processing, handling numerous calculations at once.

This technology speeds up the analysis of complex workloads, crucial for time-sensitive tasks like medical imaging or financial trading.

Scaling GPU solutions is straightforward; adding more GPUs or clusters expands system capabilities efficiently.

Accelerates AI model training, enabling the development of sophisticated AI applications.

Vital for producing high-quality 3D graphics and visual effects in gaming, simulations, and virtual reality.

Compared to CPU-only systems, GPUs achieve similar computational power more economically, reducing hardware and energy costs.

Incorporating GPUs into HPC clusters significantly enhances their calculation speed, essential for demanding computational tasks across various sectors.

Programming Models and Memory Management

Developers leverage GPU programming models to utilize its parallel processing, with popular frameworks including:

  • CUDA: Nvidia's platform for parallel computing, offering tools and libraries

  • ROCm: AMD's open-source platform for GPU computing

  • SYCL: A C++ framework for developing applications on GPUs

  • OpenCL: An open standard that supports parallel programming across different brands

GPUs feature a unique memory hierarchy to manage data efficiently, transferring it from the CPU to the GPU's memory to minimize latency and maximize performance.

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