Advantech RTX A1000 Embedded vs NVIDIA H200 SXM 141 GB

Comparison of Advantech RTX A1000 Embedded with 4 GB GDDR6 and 2,048 cores vs NVIDIA H200 SXM 141 GB with 141 GB HBM3e and 16,896 cores.

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Performance Rating

H200 H200
MI325X MI325X
A100 A100

Advantech RTX A1000 Embedded

Advantech RTX A1000 Embedded

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

NVIDIA H200 SXM 141 GB

NVIDIA H200 SXM 141 GB

Contents:

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

Memory

Memory Size

4 GB 141 GB

Memory Type

GDDR6 HBM3e

Memory Bandwidth

224.0 GB/s 4.89 TB/s

Memory Bus Width

128 бит 6,144 бит

ML Performance

FP16 (Half Precision)

6.664 TFLOPS 267.6 TFLOPS

BF16 (Brain Float)

No No

TF32 (TensorFloat)

No No

Compute Power

FP32 (Single Precision)

6.664 TFLOPS 66.91 TFLOPS

FP64 (Double Precision)

0.1041 TFLOPS 33.45 TFLOPS

CUDA Cores

2,048 16,896

RT Cores

16 No

Architecture & Compatibility

GPU Architecture

Ampere Hopper

SM (Streaming Multiprocessor)

16 132

PCIe Version

PCIe 4.0 x8 PCIe 5.0 x16

ML Software Support

CUDA Version

8.6
🔥 9.0

Clocks & Performance

Base Clock

1,192 1,500

Boost Clock

1,627 1,980

Memory Clock

1,750 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)

64 528

ROP

16 No

L2 Cache

2 MB 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

MXM Module SXM Module

Release Date

March 30, 2022 Nov. 18, 2024

Display Outputs

Portable Device Dependent
No outputs

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