NVIDIA GeForce GTS 160M vs NVIDIA H200 SXM 141 GB

Comparison of NVIDIA GeForce GTS 160M with 1,024 GB GDDR3 and 64 cores vs NVIDIA H200 SXM 141 GB with 141 GB HBM3e and 16,896 cores.

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Performance Rating

A100 A100
H200 H200
MI325X MI325X

NVIDIA GeForce GTS 160M

NVIDIA GeForce GTS 160M

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

NVIDIA H200 SXM 141 GB

67.4

NVIDIA H200 SXM 141 GB

67.4

Contents:

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

Memory

Memory Size

1,024 MB
🔥 141 ГБ

Memory Type

GDDR3 HBM3e

Memory Bandwidth

51.20 GB/s
🔥 4.89 TB/s

Memory Bus Width

256 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)

0.192 TFLOPS
🔥 +34,749% 66.91 TFLOPS

FP64 (Double Precision)

No
🔥 33.45 TFLOPS

CUDA Cores

64
🔥 +26,300% 16,896

RT Cores

No No

Architecture & Compatibility

GPU Architecture

Tesla Hopper

SM (Streaming Multiprocessor)

8
🔥 +1,550% 132

PCIe Version

PCIe 2.0 x16 PCIe 5.0 x16

ML Software Support

CUDA Version

1.1
🔥 9.0

Clocks & Performance

Base Clock

No
🔥 1,500

Boost Clock

No
🔥 1,980

Memory Clock

800
🔥 +99% 1,593

Power Consumption

TDP/TGP

🔥 -91% 60 W
700 W

Recommended PSU

No 1100 W

Power Connector

None 8-pin EPS

Rendering

Texture Units (TMU)

32
🔥 +1,550% 528

ROP

No No

L2 Cache

64 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

IGP
🔥 SXM Module

Release Date

March 3, 2009 Nov. 18, 2024

Display Outputs

Portable Device Dependent
No outputs

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