Ada Lovelace
NVIDIA L40S
wins
3
The L40S is NVIDIA's inference-optimized Ada Lovelace GPU. 48GB GDDR6 and 362 TFLOPS FP16 make it a strong choice for production inference at lower cost than H100.
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
NVIDIA L40S
Metric
NVIDIA A100 80GB
FP16 TFLOPS
Bandwidth
VRAM
TDP (lower=better)
TFLOPS/watt
GB/s per watt
Full specification table
| Spec | NVIDIA L40S | NVIDIA A100 80GB |
|---|---|---|
| Architecture | Ada Lovelace | Ampere |
| VRAM | 48 GB GDDR6 | 80 GB HBM2e |
| VRAM type | GDDR6 | HBM2e |
| Memory bandwidth | 864 GB/s | 2,039 GB/s |
| FP16 TFLOPS | 362 TFLOPS | 312 TFLOPS |
| TDP | 350 W | 400 W |
| TFLOPS/watt | 1.03 T/W | 0.78 T/W |
| GB/s per watt | 2.47 GB/s/W | 5.10 GB/s/W |
| NVLink | No | Yes |
| Release year | 2023 | 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 L40S
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 NVIDIA L40S for:
- Inference
- Video AI
- Multi-modal workloads
Choose NVIDIA A100 80GB for:
- ML training
- Inference at scale
- HPC
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