NVIDIA H100 vs NVIDIA L40S
Training powerhouse vs inference specialist
The H100 has 2.7x the FP16 performance (990 vs 366 TFLOPS) with HBM3 memory. The L40S costs a fraction of the price and is purpose-built for inference workloads.
Pricing Comparison
Specifications
| Specification | NVIDIA H100 | NVIDIA L40S |
|---|---|---|
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Hopper | Ada Lovelace |
| Accelerator Type | GPU | GPU |
| Primary Use | training | inference |
| Memory (VRAM) | 80 GB | 48 GB |
| FP16 Performance | 990 TFLOPS | 366 TFLOPS |
| TDP | 700W | 350W |
| Perf per Watt | 1.41 TFLOPS/W | 1.05 TFLOPS/W |
Detailed Analysis
The H100 and L40S are designed for different segments of the AI compute market. The H100 is a premium training GPU, while the L40S targets cost-effective inference and fine-tuning.
The H100's compute advantage (990 vs 366 TFLOPS) and HBM3 memory bandwidth (3.35 TB/s) make it unmatched for training large models. However, for inference workloads where the model fits in 48GB of memory, the L40S can serve queries at a fraction of the cost.
The price difference is substantial — the L40S typically costs 70-80% less per hour than the H100. For production inference deployments serving models up to ~25B parameters, the L40S delivers excellent cost-per-query metrics.
The decision is straightforward: if you're training, use the H100. If you're serving models in production and cost matters, the L40S is purpose-built for that role.
Verdict
H100 — not close. The compute and memory bandwidth gap is too large.
L40S for cost-effective inference. H100 only when ultra-low latency is critical.
L40S wins on inference value. H100 wins on training value. Don't use H100s for inference unless necessary.
Frequently Asked Questions
Should I use H100 or L40S for serving a 13B model?
L40S. A 13B model fits comfortably in 48GB with quantisation, and the L40S costs a fraction of the H100 per hour. Reserve H100s for training.
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