NVIDIA H200 SXM 141 GB vs Samsung RTX 4050 Max-Q

Comparison of NVIDIA H200 SXM 141 GB with 141 GB HBM3e and 16,896 cores vs Samsung RTX 4050 Max-Q with 6 GB GDDR6 and 2,560 cores.

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Performance Rating

H200 H200
MI325X MI325X
A100 A100

NVIDIA H200 SXM 141 GB

NVIDIA H200 SXM 141 GB

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

Samsung RTX 4050 Max-Q

4.6

Samsung RTX 4050 Max-Q

4.6

Contents:

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

Memory

Memory Size

141 GB
🔥 6 ГБ

Memory Type

HBM3e GDDR6

Memory Bandwidth

4.89 TB/s 192.0 GB/s

Memory Bus Width

6,144 бит 96 бит

ML Performance

FP16 (Half Precision)

267.6 TFLOPS
🔥 8.218 TFLOPS

BF16 (Brain Float)

No No

TF32 (TensorFloat)

No No

Compute Power

FP32 (Single Precision)

66.91 TFLOPS
🔥 8.218 TFLOPS

FP64 (Double Precision)

33.45 TFLOPS
🔥 +383,757% 128,400.0 TFLOPS

CUDA Cores

16,896
🔥 2,560

RT Cores

No
🔥 20

Architecture & Compatibility

GPU Architecture

Hopper Ada Lovelace

SM (Streaming Multiprocessor)

132
🔥 20

PCIe Version

PCIe 5.0 x16 PCIe 4.0 x8

ML Software Support

CUDA Version

🔥 9.0
8.9

Clocks & Performance

Base Clock

1,500
🔥 1,140

Boost Clock

1,980
🔥 1,605

Memory Clock

1,593
🔥 +26% 2,000

Power Consumption

TDP/TGP

700 W
🔥 -95% 35 W

Recommended PSU

1100 W No

Power Connector

8-pin EPS None

Rendering

Texture Units (TMU)

528
🔥 80

ROP

No
🔥 20

L2 Cache

50 MB
🔥 12 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

SXM Module IGP

Release Date

Nov. 18, 2024 Jan. 3, 2023

Display Outputs

No outputs
Portable Device Dependent

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