NVIDIA CMP 70HX vs NVIDIA Tesla M40

Comparison of NVIDIA CMP 70HX with 8 GB GDDR6X and 3,840 cores vs NVIDIA Tesla M40 with 12 GB GDDR5 and 3,072 cores.

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Performance Rating

H200 H200
MI325X MI325X
A100 A100

NVIDIA CMP 70HX

NVIDIA CMP 70HX

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

NVIDIA Tesla M40

NVIDIA Tesla M40

Contents:

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

Memory

Memory Size

8 GB 12 GB

Memory Type

GDDR6X GDDR5

Memory Bandwidth

608.3 GB/s 288.4 GB/s

Memory Bus Width

256 бит 384 бит

ML Performance

FP16 (Half Precision)

10.71 TFLOPS No

BF16 (Brain Float)

No No

TF32 (TensorFloat)

No No

Compute Power

FP32 (Single Precision)

10.71 TFLOPS 6.832 TFLOPS

FP64 (Double Precision)

0.1674 TFLOPS 0.2135 TFLOPS

CUDA Cores

3,840 3,072

RT Cores

30 No

Architecture & Compatibility

GPU Architecture

Ampere Maxwell 2.0

SM (Streaming Multiprocessor)

30 No

PCIe Version

PCIe 1.0 x4 PCIe 3.0 x16

ML Software Support

CUDA Version

🔥 8.6
5.2

Clocks & Performance

Base Clock

1,365 948

Boost Clock

1,395 1,112

Memory Clock

1,188 1,502

Power Consumption

TDP/TGP

unknown 250 W

Recommended PSU

🔥 -67% 200 W
600 W

Power Connector

1x 12-pin 8-pin EPS

Rendering

Texture Units (TMU)

120 192

ROP

30 No

L2 Cache

4 MB 3 MB

Benchmarks

llama.cpp, gpt-oss 20B Q4_K - Medium

47.0 tokens/s

llama.cpp, llama 7B Q4_0

36.7 tokens/s

llama.cpp, llama-2-7b-Q4_0

41.7 tokens/s

llama.cpp, qwen3 32B Q4_K - Medium

7.19 tokens/s

llama.cpp, qwen3moe 30B.A3B Q4_K - Medium

35.1 tokens/s

Additional

Slots

Dual-slot Dual-slot

Release Date

March 11, 2021 Nov. 10, 2015

Display Outputs

No outputs
No outputs

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