Hopper
NVIDIA H100 PCIe 80GB
wins
3
The H100 PCIe is the more affordable form factor of the H100.
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 H100 PCIe 80GB
Metric
NVIDIA L4
FP16 TFLOPS
Bandwidth
VRAM
TDP (lower=better)
TFLOPS/watt
GB/s per watt
Full specification table
| Spec | NVIDIA H100 PCIe 80GB | NVIDIA L4 |
|---|---|---|
| Architecture | Hopper | Ada Lovelace |
| VRAM | 80 GB HBM2e | 24 GB GDDR6 |
| VRAM type | HBM2e | GDDR6 |
| Memory bandwidth | 2,000 GB/s | 300 GB/s |
| FP16 TFLOPS | 756 TFLOPS | 242 TFLOPS |
| TDP | 350 W | 72 W |
| TFLOPS/watt | 2.16 T/W | 3.36 T/W |
| GB/s per watt | 5.71 GB/s/W | 4.17 GB/s/W |
| NVLink | No | No |
| Release year | 2022 | 2023 |
Live cloud pricing
On-demand pricing across providers — updated in real time
Power efficiency analysis
TFLOPS/watt and GB/s/watt — critical for data center TCO
NVIDIA H100 PCIe 80GB
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.
When to choose each GPU
Choose NVIDIA H100 PCIe 80GB for:
- Budget Hopper inference
- PCIe server deployments
- Cost-sensitive LLM serving
Weaknesses / Limitations
- Lower FP16 TFLOPS (756) and bandwidth (2,000 GB/s) than H100 SXM5
- No NVLink — cannot scale across cards for very large models
- Higher cost than A100 with only moderate performance advantage
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
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