CDNA 2
AMD Instinct MI250X
wins
3
The AMD Instinct MI250X is AMD's CDNA 2 data center GPU.
Ampere
NVIDIA A100 40GB
wins
2
The A100 40GB is the more affordable sibling of the A100 80GB.
Performance comparison
Visual bars — winner highlighted
AMD Instinct MI250X
Metric
NVIDIA A100 40GB
FP16 TFLOPS
Bandwidth
VRAM
TDP (lower=better)
TFLOPS/watt
GB/s per watt
Full specification table
| Spec | AMD Instinct MI250X | NVIDIA A100 40GB |
|---|---|---|
| Architecture | CDNA 2 | Ampere |
| VRAM | 128 GB HBM2e | 40 GB HBM2e |
| VRAM type | HBM2e | HBM2e |
| Memory bandwidth | 3,277 GB/s | 1,555 GB/s |
| FP16 TFLOPS | 383 TFLOPS | 312 TFLOPS |
| TDP | 560 W | 400 W |
| TFLOPS/watt | 0.68 T/W | 0.78 T/W |
| GB/s per watt | 5.85 GB/s/W | 3.89 GB/s/W |
| NVLink | No | Yes |
| Release year | 2021 | 2020 |
Live cloud pricing
On-demand pricing across providers — updated in real time
AMD Instinct MI250X
No live pricing data available.
NVIDIA A100 40GB
Power efficiency analysis
TFLOPS/watt and GB/s/watt — critical for data center TCO
AMD Instinct MI250X
NVIDIA A100 40GB
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.
Performance-per-dollar
TFLOPS/$ and GB/s/$ at current best cloud prices
AMD Instinct MI250X
NVIDIA A100 40GB
TFLOPS/$ = FP16 TFLOPS ÷ best on-demand $/hr. Higher is better — more compute per dollar. GB/s/$ = memory bandwidth ÷ best on-demand $/hr. For memory-bound inference workloads (most LLM serving), GB/s/$ is often a better predictor of cost-efficiency than TFLOPS/$. Prices are live from the Compute Comparison API and update in real time.
Workload Cost Comparison
Total job cost = best on-demand $/hr × GPU count × hours
| Workload | Scale | AMD Instinct MI250X | NVIDIA A100 40GB |
|---|---|---|---|
| 7B LLM fine-tune | 8× GPU, 24 h | — | $56 |
| 70B LLM fine-tune | 64× GPU, 72 h | — | $1.3k |
| Inference server | 1× GPU, 730 h | — | $212 |
| Image gen batch | 4× GPU, 8 h | — | $9 |
Based on current best on-demand prices. Spot pricing typically 40–70% lower. Actual cost depends on provider, region, and availability.
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 A100 40GB for:
- ML training
- Inference
- Cost-effective HPC
Weaknesses / Limitations
- 40 GB VRAM is a hard ceiling — cannot run models that require 80 GB
- Lower bandwidth than A100 80GB (1,555 vs 2,039 GB/s)
- Older Ampere architecture — lower TFLOPS than H100
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