How does AMD Instinct MI250X compare to NVIDIA H100 PCIe 80GB in memory bandwidth?

The AMD Instinct MI250X has 3,277 GB/s memory bandwidth, while the NVIDIA H100 PCIe 80GB has 2,000 GB/s. Higher bandwidth benefits memory-bound workloads like large-batch LLM inference.

Which is more power-efficient, AMD Instinct MI250X or NVIDIA H100 PCIe 80GB?

Compare AMD Instinct MI250X (383 TFLOPS FP16, 560 W TDP) vs NVIDIA H100 PCIe 80GB (756 TFLOPS FP16, 350 W TDP) for power efficiency. Lower TDP with higher TFLOPS means better TFLOPS/watt.

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CDNA 2

AMD Instinct MI250X

wins

The AMD Instinct MI250X is AMD's CDNA 2 data center GPU.

HPC workloadsLarge model inferenceAMD ROCm deployments

Hopper

NVIDIA H100 PCIe 80GB

wins

The H100 PCIe is the more affordable form factor of the H100.

Budget Hopper inferencePCIe server deploymentsCost-sensitive LLM serving

Performance comparison

Visual bars — winner highlighted

AMD Instinct MI250X

Metric

NVIDIA H100 PCIe 80GB

FP16 TFLOPS

383 T

756 T

Bandwidth

3.3k GB/s

2.0k GB/s

VRAM

1.3k GB

802 GB

TDP (lower=better)

560 W

350 W

TFLOPS/watt

0.68

2.16

GB/s per watt

5.85

5.71

Full specification table

Spec	AMD Instinct MI250X	NVIDIA H100 PCIe 80GB
Architecture	CDNA 2	Hopper
VRAM	128 GB HBM2e	80 GB HBM2e
VRAM type	HBM2e	HBM2e
Memory bandwidth	3,277 GB/s	2,000 GB/s
FP16 TFLOPS	383 TFLOPS	756 TFLOPS
TDP	560 W	350 W
TFLOPS/watt	0.68 T/W	2.16 T/W
GB/s per watt	5.85 GB/s/W	5.71 GB/s/W
NVLink	No	No
Release year	2021	2022

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Power efficiency analysis

TFLOPS/watt and GB/s/watt — critical for data center TCO

AMD Instinct MI250X

FP16 TFLOPS383 TFLOPS

TDP560 W

TFLOPS/watt0.684 T/W

GB/s per watt5.85 GB/s/W

NVIDIA H100 PCIe 80GB

FP16 TFLOPS756 TFLOPS

TDP350 W

TFLOPS/watt2.160 T/W

GB/s per watt5.71 GB/s/W

TFLOPS/watt measures compute efficiency — how much AI throughput you get per watt of power consumed. For data centers with PUE of 1.2–1.5, a 10% improvement in TFLOPS/watt translates directly to lower electricity costs and cooling requirements. GB/s/watt measures memory bandwidth efficiency, which is the binding constraint for memory-bound LLM inference workloads.

When to choose each GPU

Choose AMD Instinct MI250X for:

HPC workloads
Large model inference
AMD ROCm deployments

Weaknesses / Limitations

Older CDNA 2 architecture — lower FP16 TFLOPS than MI300X or H100
ROCm software ecosystem less mature than CUDA
560W TDP — high power draw for its performance tier

Choose NVIDIA H100 PCIe 80GB for:

Budget Hopper inference
PCIe server deployments
Cost-sensitive LLM serving

Weaknesses / Limitations

Lower FP16 TFLOPS (756) and bandwidth (2,000 GB/s) than H100 SXM5
No NVLink — cannot scale across cards for very large models
Higher cost than A100 with only moderate performance advantage

Popular GPU comparisons

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