NVIDIA H200 SXM 141 GB vs NVIDIA RTX PRO 6000 Blackwell Max-Q

Comparison of NVIDIA H200 SXM 141 GB with 141 GB HBM3e and 16,896 cores vs NVIDIA RTX PRO 6000 Blackwell Max-Q with 96 GB GDDR7 and 24,064 cores.

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Performance Rating

NVIDIA H200 SXM 141 GB outperforms NVIDIA RTX PRO 6000 Blackwell Max-Q by 26.27% in the overall GPU ARK 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 RTX PRO 6000 Blackwell Max-Q

53.4

NVIDIA RTX PRO 6000 Blackwell Max-Q

53.4

Contents:

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

Memory

Memory Size

🔥 +47% 141 ГБ
96 ГБ

Memory Type

HBM3e GDDR7

Memory Bandwidth

🔥 +173% 4.89 TB/s
1.79 TB/s

Memory Bus Width

6,144 бит 512 бит

ML Performance

FP16 (Half Precision)

🔥 +143% 267.6 TFLOPS
110.1 TFLOPS

BF16 (Brain Float)

No No

TF32 (TensorFloat)

No No

Compute Power

FP32 (Single Precision)

66.91 TFLOPS
🔥 +65% 110.1 TFLOPS

FP64 (Double Precision)

🔥 +1,844% 33.45 TFLOPS
1.721 TFLOPS

CUDA Cores

16,896
🔥 +42% 24,064

RT Cores

No
🔥 188

Architecture & Compatibility

GPU Architecture

Hopper Blackwell 2.0

SM (Streaming Multiprocessor)

132
🔥 +42% 188

PCIe Version

PCIe 5.0 x16 PCIe 5.0 x16

ML Software Support

CUDA Version

9.0
🔥 12.0

Clocks & Performance

Base Clock

1,500
🔥 +6% 1,590

Boost Clock

1,980
🔥 +16% 2,288

Memory Clock

1,593
🔥 +10% 1,750

Power Consumption

TDP/TGP

700 W
🔥 -57% 300 W

Recommended PSU

1100 W
🔥 -36% 700 W

Power Connector

8-pin EPS 1x 16-pin

Rendering

Texture Units (TMU)

528
🔥 +42% 752

ROP

No
🔥 188

L2 Cache

🔥 50 MB
128 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

llama.cpp, llama 7B Q4_0

270.0 tokens/s

Geekbench AI, FP16

58 178 points

Geekbench AI, INT8

29 188 points

Geekbench AI, FP32

39 339 points

MLPerf, deepseek-r1 (fp8)

1 113 tokens/s

MLPerf, mixtral-8x7b (fp8)

7 132 tokens/s

Additional

Slots

🔥 SXM Module
Dual-slot

Release Date

Nov. 18, 2024 March 18, 2025

Display Outputs

No outputs
4x DisplayPort 2.1b

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