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.
Ampere
NVIDIA A100 40GB
wins
0
The A100 40GB is the more affordable sibling of the A100 80GB.
Performance comparison
Visual bars — winner highlighted
NVIDIA A100 80GB
Metric
NVIDIA A100 40GB
FP16 TFLOPS
Bandwidth
VRAM
TDP (lower=better)
TFLOPS/watt
GB/s per watt
Full specification table
| Spec | NVIDIA A100 80GB | NVIDIA A100 40GB |
|---|---|---|
| Architecture | Ampere | Ampere |
| VRAM | 80 GB HBM2e | 40 GB HBM2e |
| VRAM type | HBM2e | HBM2e |
| Memory bandwidth | 2,039 GB/s | 1,555 GB/s |
| FP16 TFLOPS | 312 TFLOPS | 312 TFLOPS |
| TDP | 400 W | 400 W |
| TFLOPS/watt | 0.78 T/W | 0.78 T/W |
| GB/s per watt | 5.10 GB/s/W | 3.89 GB/s/W |
| NVLink | Yes | Yes |
| Release year | 2020 | 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
NVIDIA A100 80GB
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.
When to choose each GPU
Choose NVIDIA A100 80GB for:
- ML training
- Inference at scale
- HPC
Choose NVIDIA A100 40GB for:
- ML training
- Inference
- Cost-effective HPC
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