CDNA 2
AMD Instinct MI250X
wins
3
The AMD Instinct MI250X is AMD's CDNA 2 data center GPU.
Ampere
NVIDIA A100 80GB
wins
2
The A100 remains one of the most widely available data center GPUs. Its broad provider support and mature software ecosystem make it a reliable choice for ML training and inference.
Performance comparison
Visual bars — winner highlighted
AMD Instinct MI250X
Metric
NVIDIA A100 80GB
FP16 TFLOPS
Bandwidth
VRAM
TDP (lower=better)
TFLOPS/watt
GB/s per watt
Full specification table
| Spec | AMD Instinct MI250X | NVIDIA A100 80GB |
|---|---|---|
| Architecture | CDNA 2 | Ampere |
| VRAM | 128 GB HBM2e | 80 GB HBM2e |
| VRAM type | HBM2e | HBM2e |
| Memory bandwidth | 3,277 GB/s | 2,039 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 | 5.10 GB/s/W |
| NVLink | No | Yes |
| Release year | 2021 | 2020 |
Live cloud pricing
On-demand $/hr across providers — updated in real time
Power efficiency analysis
TFLOPS/watt and GB/s/watt — critical for data center TCO
AMD Instinct MI250X
NVIDIA A100 80GB
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
Choose NVIDIA A100 80GB for:
- ML training
- Inference at scale
- HPC
Popular GPU comparisons