CDNA 2
AMD Instinct MI250X
wins
2
The AMD Instinct MI250X is AMD's CDNA 2 data center GPU.
Hopper
NVIDIA H100 SXM5 80GB
wins
3
The H100 SXM5 is the high-performance form factor of the H100.
Performance comparison
Visual bars — winner highlighted
AMD Instinct MI250X
Metric
NVIDIA H100 SXM5 80GB
FP16 TFLOPS
Bandwidth
VRAM
TDP (lower=better)
TFLOPS/watt
GB/s per watt
Full specification table
| Spec | AMD Instinct MI250X | NVIDIA H100 SXM5 80GB |
|---|---|---|
| Architecture | CDNA 2 | Hopper |
| VRAM | 128 GB HBM2e | 80 GB HBM3 |
| VRAM type | HBM2e | HBM3 |
| Memory bandwidth | 3,277 GB/s | 3,350 GB/s |
| FP16 TFLOPS | 383 TFLOPS | 989 TFLOPS |
| TDP | 560 W | 700 W |
| TFLOPS/watt | 0.68 T/W | 1.41 T/W |
| GB/s per watt | 5.85 GB/s/W | 4.79 GB/s/W |
| NVLink | No | Yes |
| Release year | 2021 | 2022 |
Live cloud pricing
On-demand pricing across providers — updated in real time
Power efficiency analysis
TFLOPS/watt and GB/s/watt — critical for data center TCO
AMD Instinct MI250X
NVIDIA H100 SXM5 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
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 SXM5 80GB for:
- LLM training
- Multi-GPU NVLink clusters
- High-throughput inference
Weaknesses / Limitations
- Highest cost per H100 variant — premium over PCIe form factor
- SXM5 form factor requires NVLink-capable server chassis
- 700W TDP demands high-density power and cooling infrastructure
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