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.
Ada Lovelace
NVIDIA L40S
wins
4
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.
Performance comparison
Visual bars — winner highlighted
NVIDIA A100 80GB
Metric
NVIDIA L40S
FP16 TFLOPS
Bandwidth
VRAM
TDP (lower=better)
TFLOPS/watt
GB/s per watt
TFLOPS/$
FP16 TFLOPS ÷ best on-demand $/hr
GB/s per $
Memory bandwidth ÷ best on-demand $/hr
Full specification table
| Spec | NVIDIA A100 80GB | NVIDIA L40S |
|---|---|---|
| Architecture | Ampere | Ada Lovelace |
| VRAM | 80 GB HBM2e | 48 GB GDDR6 |
| VRAM type | HBM2e | GDDR6 |
| Memory bandwidth | 2,039 GB/s | 864 GB/s |
| FP16 TFLOPS | 312 TFLOPS | 362 TFLOPS |
| TDP | 400 W | 350 W |
| TFLOPS/watt | 0.78 T/W | 1.03 T/W |
| GB/s per watt | 5.10 GB/s/W | 2.47 GB/s/W |
| NVLink | Yes | No |
| Release year | 2020 | 2023 |
| Best cloud price | $0.48/hr | $0.38/hr |
| TFLOPS/$ | 650.0 | 952.6 |
| GB/s per $ | 4248 | 2274 |
Live cloud pricing
On-demand pricing across providers — updated in real time
NVIDIA A100 80GB
NVIDIA L40S
Salad: Lowest priority tier — preemptible
Power efficiency analysis
TFLOPS/watt and GB/s/watt — critical for data center TCO
NVIDIA A100 80GB
NVIDIA L40S
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.
Performance-per-dollar
TFLOPS/$ and GB/s/$ at current best cloud prices
NVIDIA A100 80GB
NVIDIA L40S
TFLOPS/$ = FP16 TFLOPS ÷ best on-demand $/hr. Higher is better — more compute per dollar. GB/s/$ = memory bandwidth ÷ best on-demand $/hr. For memory-bound inference workloads (most LLM serving), GB/s/$ is often a better predictor of cost-efficiency than TFLOPS/$. Prices are live from the Compute Comparison API and update in real time.
Workload Cost Comparison
Total job cost = best on-demand $/hr × GPU count × hours
NVIDIA A100 80GB costs 1.3× more at current best prices. NVIDIA A100 80GB has 802GB VRAM vs 486GB — larger models fit without offloading; 2.4× higher memory bandwidth (2039 vs 864 GB/s) improves token throughput for memory-bound inference.
| Workload | Scale | NVIDIA A100 80GB | NVIDIA L40S |
|---|---|---|---|
| 7B LLM fine-tune | 8× GPU, 24 h | $92 | $73▼ |
| 70B LLM fine-tune | 64× GPU, 72 h | $2.2k | $1.8k▼ |
| Inference server | 1× GPU, 730 h | $350 | $277▼ |
| Image gen batch | 4× GPU, 8 h | $15 | $12▼ |
Based on current best on-demand prices. Spot pricing typically 40–70% lower. Actual cost depends on provider, region, and availability.
When to choose each GPU
Choose NVIDIA A100 80GB for:
- ML training
- Inference at scale
- HPC
Weaknesses / Limitations
- Older Ampere architecture — lower FP16 TFLOPS than H100 at similar price
- No Ada Lovelace efficiency improvements
- Being phased out by H100 on many platforms
Choose NVIDIA L40S for:
- Inference
- Video AI
- Multi-modal workloads
Weaknesses / Limitations
- GDDR6 bandwidth (864 GB/s) is lower than HBM-based GPUs — bottleneck for memory-bound LLM serving
- No NVLink — cannot scale across cards for very large models
- Less suitable for large-scale training than H100 or A100
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