NVIDIA H200 Tensor Core GPU
Supercharging AI and HPC workloads.
The GPU for Generative AI and HPC
The NVIDIA H200 Tensor Core GPU supercharges generative AI and high-performance computing (HPC) workloads with game-changing performance and memory capabilities. As the first GPU with HBM3e, the H200’s larger and faster memory fuels the acceleration of generative AI and large language models (LLMs) while advancing scientific computing for HPC workloads.
Highlights
Experience Next-Level Performance
Llama2 70B Inference
1.9X Faster
GPT-3 175B Inference
1.6X Faster
High-Performance Computing
110X Faster
Benefits
Higher Performance With Larger, Faster Memory
Based on the NVIDIA Hopper™ architecture, the NVIDIA H200 is the first GPU to offer 141 gigabytes (GB) of HBM3e memory at 4.8 terabytes per second (TB/s) —that’s nearly double the capacity of the NVIDIA H100 Tensor Core GPU with 1.4X more memory bandwidth. The H200’s larger and faster memory accelerates generative AI and LLMs, while advancing scientific computing for HPC workloads with better energy efficiency and lower total cost of ownership.
Preliminary specifications. May be subject to change.
Llama2 13B: ISL 128, OSL 2K | Throughput | H100 SXM 1x GPU BS 64 | H200 SXM 1x GPU BS 128
GPT-3 175B: ISL 80, OSL 200 | x8 H100 SXM GPUs BS 64 | x8 H200 SXM GPUs BS 128
Llama2 70B: ISL 2K, OSL 128 | Throughput | H100 SXM 1x GPU BS 8 | H200 SXM 1x GPU BS 32.
Unlock Insights With High-Performance LLM Inference
In the ever-evolving landscape of AI, businesses rely on LLMs to address a diverse range of inference needs. An AI inference accelerator must deliver the highest throughput at the lowest TCO when deployed at scale for a massive user base.
The H200 boosts inference speed by up to 2X compared to H100 GPUs when handling LLMs like Llama2.
Supercharge High-Performance Computing
Memory bandwidth is crucial for HPC applications as it enables faster data transfer, reducing complex processing bottlenecks. For memory-intensive HPC applications like simulations, scientific research, and artificial intelligence, the H200’s higher memory bandwidth ensures that data can be accessed and manipulated efficiently, leading up to 110X faster time to results compared to CPUs.
Preliminary specifications. May be subject to change.
HPC MILC- dataset NERSC Apex Medium | HGX H200 4-GPU | dual Sapphire Rapids 8480
HPC Apps- CP2K: dataset H2O-32-RI-dRPA-96points | GROMACS: dataset STMV | ICON: dataset r2b5 | MILC: dataset NERSC Apex Medium | Chroma: dataset HMC Medium | Quantum Espresso: dataset AUSURF112 | 1x H100 SXM | 1x H200 SXM.
AI Acceleration for Mainstream Enterprise Servers with H200 NVL
NVIDIA H200 NVL is ideal for lower-power, air-cooled enterprise rack designs that require flexible configurations, delivering acceleration for every AI and HPC workload regardless of size. With up to four GPUs connected by NVIDIA NVLink™ and a 1.5X memory increase, large language model (LLM) inference can be accelerated up to 1.7X and HPC applications achieve up to 1.3X more performance over the H100 NVL.
Enterprise-Ready: AI Software Streamlines Development and Deployment
NVIDIA H200 NVL comes with a five-year NVIDIA AI Enterprise subscription and simplifies the way you build an enterprise AI-ready platform. H200 accelerates AI development and deployment for production-ready generative AI solutions, including computer vision, speech AI, retrieval augmented generation (RAG), and more. NVIDIA AI Enterprise includes NVIDIA NIM™, a set of easy-to-use microservices designed to speed up enterprise generative AI deployment. Together, deployments have enterprise-grade security, manageability, stability, and support. This results in performance-optimized AI solutions that deliver faster business value and actionable insights.
Specifications
NVIDIA H200 Tensor Core GPU
| 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 |
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1 Preliminary specifications. May be subject to change. |
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