NVIDIA H100 SXM5 94 GB vs NVIDIA H200 SXM 141 GB

Comparison of NVIDIA H100 SXM5 94 GB with 94 GB HBM3 and 16,896 cores vs NVIDIA H200 SXM 141 GB with 141 GB HBM3e and 16,896 cores.

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Performance Rating

H200 H200
MI325X MI325X
A100 A100

NVIDIA H100 SXM5 94 GB

NVIDIA H100 SXM5 94 GB

RX 7900 XTX RX 7900 XTX
Instinct MI300X Instinct MI300X
MI250 MI250

NVIDIA H200 SXM 141 GB

NVIDIA H200 SXM 141 GB

Contents:

Memory ML Performance Compute Power Architecture & Compatibility ML Software Support Clocks & Performance Power Consumption Rendering Benchmarks Additional

Memory

Memory Size

94 GB 141 GB

Memory Type

HBM3 HBM3e

Memory Bandwidth

3.36 TB/s 4.89 TB/s

Memory Bus Width

5,120 бит 6,144 бит

ML Performance

FP16 (Half Precision)

267.6 TFLOPS 267.6 TFLOPS

BF16 (Brain Float)

No No

TF32 (TensorFloat)

No No

Compute Power

FP32 (Single Precision)

66.91 TFLOPS 66.91 TFLOPS

FP64 (Double Precision)

33.45 TFLOPS 33.45 TFLOPS

CUDA Cores

16,896 16,896

RT Cores

No No

Architecture & Compatibility

GPU Architecture

Hopper Hopper

SM (Streaming Multiprocessor)

132 132

PCIe Version

PCIe 5.0 x16 PCIe 5.0 x16

ML Software Support

CUDA Version

9.0 9.0

Clocks & Performance

Base Clock

1,350 1,500

Boost Clock

1,980 1,980

Memory Clock

1,313 1,593

Power Consumption

TDP/TGP

700 W 700 W

Recommended PSU

1100 W 1100 W

Power Connector

8-pin EPS 8-pin EPS

Rendering

Texture Units (TMU)

528 528

ROP

No No

L2 Cache

50 MB 50 MB

Benchmarks

MLPerf, llama2-70b-99.9 (UNSET)

3 534 tokens/s

MLPerf, llama2-70b-99.9 (fp16)

3 553 tokens/s

MLPerf, llama2-70b-99.9 (fp8)

2 106 tokens/s
🔥 +16% 2 444 tokens/s

MLPerf, llama3.1-405b (fp16)

40.8 tokens/s

MLPerf, llama3.1-405b (fp8)

25.3 tokens/s

MLPerf, llama3.1-8b (fp8)

5 161 tokens/s

MLPerf, deepseek-r1 (fp8)

1 113 tokens/s

MLPerf, mixtral-8x7b (fp8)

4 714 tokens/s
🔥 +51% 7 132 tokens/s

Additional

Slots

SXM Module SXM Module

Release Date

March 21, 2023 Nov. 18, 2024

Display Outputs

No outputs
No outputs

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