Ampere
NVIDIA A100 80GB
wins
1
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.
Hopper
NVIDIA H100 80GB
wins
4
The NVIDIA H100 is the gold standard for AI training and inference. With 80GB HBM3 memory and NVLink 4.0 support, it dominates large-model training and multi-GPU clusters.
Performance comparison
Visual bars — winner highlighted
NVIDIA A100 80GB
Metric
NVIDIA H100 80GB
FP16 TFLOPS
Bandwidth
VRAM
TDP (lower=better)
TFLOPS/watt
GB/s per watt
Full specification table
| Spec | NVIDIA A100 80GB | NVIDIA H100 80GB |
|---|---|---|
| Architecture | Ampere | Hopper |
| VRAM | 80 GB HBM2e | 80 GB HBM3 |
| VRAM type | HBM2e | HBM3 |
| Memory bandwidth | 2,039 GB/s | 3,350 GB/s |
| FP16 TFLOPS | 312 TFLOPS | 989 TFLOPS |
| TDP | 400 W | 700 W |
| TFLOPS/watt | 0.78 T/W | 1.41 T/W |
| GB/s per watt | 5.10 GB/s/W | 4.79 GB/s/W |
| NVLink | Yes | Yes |
| Release year | 2020 | 2022 |
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
NVIDIA A100 80GB
NVIDIA H100 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 NVIDIA A100 80GB for:
- ML training
- Inference at scale
- HPC
Choose NVIDIA H100 80GB for:
- LLM training
- Large-scale inference
- HPC workloads
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