Ampere
NVIDIA A100 40GB
wins
3
The A100 40GB is the more affordable sibling of the A100 80GB.
Ada Lovelace
NVIDIA L4
wins
2
The NVIDIA L4 is the most power-efficient Ada Lovelace GPU at just 72W TDP.
Performance comparison
Visual bars — winner highlighted
NVIDIA A100 40GB
Metric
NVIDIA L4
FP16 TFLOPS
Bandwidth
VRAM
TDP (lower=better)
TFLOPS/watt
GB/s per watt
Full specification table
| Spec | NVIDIA A100 40GB | NVIDIA L4 |
|---|---|---|
| Architecture | Ampere | Ada Lovelace |
| VRAM | 40 GB HBM2e | 24 GB GDDR6 |
| VRAM type | HBM2e | GDDR6 |
| Memory bandwidth | 1,555 GB/s | 300 GB/s |
| FP16 TFLOPS | 312 TFLOPS | 242 TFLOPS |
| TDP | 400 W | 72 W |
| TFLOPS/watt | 0.78 T/W | 3.36 T/W |
| GB/s per watt | 3.89 GB/s/W | 4.17 GB/s/W |
| NVLink | Yes | No |
| Release year | 2020 | 2023 |
Live cloud pricing
On-demand pricing across providers — updated in real time
NVIDIA A100 40GB
NVIDIA L4
No live pricing data available.
Power efficiency analysis
TFLOPS/watt and GB/s/watt — critical for data center TCO
NVIDIA A100 40GB
NVIDIA L4
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 40GB
NVIDIA L4
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
| Workload | Scale | NVIDIA A100 40GB | NVIDIA L4 |
|---|---|---|---|
| 7B LLM fine-tune | 8× GPU, 24 h | $56 | — |
| 70B LLM fine-tune | 64× GPU, 72 h | $1.3k | — |
| Inference server | 1× GPU, 730 h | $212 | — |
| Image gen batch | 4× GPU, 8 h | $9 | — |
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 40GB for:
- ML training
- Inference
- Cost-effective HPC
Weaknesses / Limitations
- 40 GB VRAM is a hard ceiling — cannot run models that require 80 GB
- Lower bandwidth than A100 80GB (1,555 vs 2,039 GB/s)
- Older Ampere architecture — lower TFLOPS than H100
Choose NVIDIA L4 for:
- Efficient inference
- Video transcoding
- Edge AI
Weaknesses / Limitations
- 24 GB VRAM limits model size — cannot run 34B+ models
- Low memory bandwidth (300 GB/s) — bottleneck for throughput-sensitive inference
- Not suitable for training or fine-tuning beyond small models
Popular GPU comparisons