NVIDIA H200 SXM 141 GB vs NVIDIA Quadro DCC

Comparison of NVIDIA H200 SXM 141 GB with 141 GB HBM3e and 16,896 cores vs NVIDIA Quadro DCC with 64 GB SDR.

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Performance Rating

A100 A100
H200 H200
MI325X MI325X

NVIDIA H200 SXM 141 GB

67.4

NVIDIA H200 SXM 141 GB

67.4
RX 7900 XTX RX 7900 XTX
MI250 MI250
Instinct MI300X Instinct MI300X

NVIDIA Quadro DCC

0.0

NVIDIA Quadro DCC

0.0

Contents:

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

Memory

Memory Size

🔥 +120% 141 ГБ
64 ГБ

Memory Type

HBM3e SDR

Memory Bandwidth

🔥 +33% 4.89 TB/s
3.680 GB/s

Memory Bus Width

6,144 бит 128 бит

ML Performance

FP16 (Half Precision)

🔥 267.6 TFLOPS
No

BF16 (Brain Float)

No No

TF32 (TensorFloat)

No No

Compute Power

FP32 (Single Precision)

🔥 66.91 TFLOPS
No

FP64 (Double Precision)

🔥 33.45 TFLOPS
No

CUDA Cores

🔥 16,896
No

RT Cores

No No

Architecture & Compatibility

GPU Architecture

Hopper Kelvin

SM (Streaming Multiprocessor)

🔥 132
No

PCIe Version

PCIe 5.0 x16 AGP 4x

ML Software Support

CUDA Version

9.0 No

Clocks & Performance

Base Clock

🔥 1,500
No

Boost Clock

🔥 1,980
No

Memory Clock

🔥 +593% 1,593
230

Power Consumption

TDP/TGP

700 W unknown

Recommended PSU

1100 W
🔥 -82% 200 W

Power Connector

8-pin EPS None

Rendering

Texture Units (TMU)

🔥 +13,100% 528
4

ROP

No No

L2 Cache

🔥 50 MB
No

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

🔥 SXM Module
Single-slot

Release Date

Nov. 18, 2024 May 14, 2001

Display Outputs

No outputs
1x DVI
1x VGA
1x S-Video

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