Mercury GSC6201 6U OpenVPX vs NVIDIA H200 SXM 141 GB

Comparison of Mercury GSC6201 6U OpenVPX with 2 GB GDDR5 and 240 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

Mercury GSC6201 6U OpenVPX

Mercury GSC6201 6U OpenVPX

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

2 GB ×2 (4 ГБ) 141 GB

Memory Type

GDDR5 HBM3e

Memory Bandwidth

80.00 GB/s ×2 (160 GB/s) 4.89 TB/s

Memory Bus Width

256 бит ×2 (512 бит) 6,144 бит

ML Performance

FP16 (Half Precision)

No 267.6 TFLOPS

BF16 (Brain Float)

No No

TF32 (TensorFloat)

No No

Compute Power

FP32 (Single Precision)

0.432 TFLOPS 66.91 TFLOPS

FP64 (Double Precision)

0.036 TFLOPS 33.45 TFLOPS

CUDA Cores

240 ×2 (480) 16,896

RT Cores

No No

Architecture & Compatibility

GPU Architecture

Fermi Hopper

SM (Streaming Multiprocessor)

5 132

PCIe Version

MXM-B (3.0) PCIe 5.0 x16

ML Software Support

CUDA Version

2.1
🔥 9.0

Clocks & Performance

Base Clock

No 1,500

Boost Clock

No 1,980

Memory Clock

625 1,593

Power Consumption

TDP/TGP

🔥 -79% 150 W
700 W

Recommended PSU

No 1100 W

Power Connector

None 8-pin EPS

Rendering

Texture Units (TMU)

40 ×2 (80) 528

ROP

No No

L2 Cache

512 KB 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)

2 444 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)

7 132 tokens/s

Additional

Slots

MXM Module SXM Module

Release Date

Feb. 22, 2011 Nov. 18, 2024

Display Outputs

Portable Device Dependent
No outputs

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