How does AMD Instinct MI250X compare to NVIDIA GB200 NVL72 in memory bandwidth?

The AMD Instinct MI250X has 3,277 GB/s memory bandwidth, while the NVIDIA GB200 NVL72 has 16,000 GB/s. Higher bandwidth benefits memory-bound workloads like large-batch LLM inference.

Which is more power-efficient, AMD Instinct MI250X or NVIDIA GB200 NVL72?

Compare AMD Instinct MI250X (383 TFLOPS FP16, 560 W TDP) vs NVIDIA GB200 NVL72 (9,000 TFLOPS FP16, 2,700 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

Blackwell

NVIDIA GB200 NVL72

wins

The GB200 NVL72 is NVIDIA's most powerful AI system.

Trillion-parameter trainingAI factory scaleNext-gen LLM inference

Performance comparison

Visual bars — winner highlighted

AMD Instinct MI250X

Metric

NVIDIA GB200 NVL72

FP16 TFLOPS

383 T

9.0k T

Bandwidth

3.3k GB/s

16.0k GB/s

VRAM

1.3k GB

3.8k GB

TDP (lower=better)

560 W

2.7k W

TFLOPS/watt

0.68

3.33

GB/s per watt

5.85

5.93

Full specification table

Spec	AMD Instinct MI250X	NVIDIA GB200 NVL72
Architecture	CDNA 2	Blackwell
VRAM	128 GB HBM2e	384 GB HBM3e (per GPU)
VRAM type	HBM2e	HBM3e (per GPU)
Memory bandwidth	3,277 GB/s	16,000 GB/s
FP16 TFLOPS	383 TFLOPS	9,000 TFLOPS
TDP	560 W	2,700 W
TFLOPS/watt	0.68 T/W	3.33 T/W
GB/s per watt	5.85 GB/s/W	5.93 GB/s/W
NVLink	No	Yes
Release year	2021	2025

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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 GB200 NVL72

FP16 TFLOPS9,000 TFLOPS

TDP2,700 W

TFLOPS/watt3.333 T/W

GB/s per watt5.93 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 GB200 NVL72 for:

Trillion-parameter training
AI factory scale
Next-gen LLM inference

Weaknesses / Limitations

Rack-scale system — not a single GPU, requires full NVL72 rack deployment
2,700W system TDP demands dedicated power and liquid cooling infrastructure
Extremely limited availability — primarily hyperscaler and large enterprise

Popular GPU comparisons

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