A100 vs H100: Is the H100 Worth 2x the Price?
Comparing NVIDIA A100 and H100 on raw FLOPs, memory bandwidth, real inference throughput, and total cost per million tokens.
The H100 launched as NVIDIA's first Hopper-architecture datacenter GPU and has spent the last two years redefining what's possible in transformer inference. The A100 is still everywhere — cheaper, plentiful, and battle-tested. So which one should you actually rent?
Spec-sheet TL;DR
| A100 80GB | H100 80GB | |
|---|---|---|
| Architecture | Ampere | Hopper |
| FP16 Tensor Core | 312 TFLOPS | 989 TFLOPS |
| FP8 Tensor Core | — | 1,979 TFLOPS |
| HBM | 80 GB HBM2e | 80 GB HBM3 |
| Memory bandwidth | 2.0 TB/s | 3.35 TB/s |
| NVLink | 600 GB/s | 900 GB/s |
| Typical rent | ~$1.20–1.90/hr | ~$2.50–3.50/hr |
Where the H100 wins
For modern transformer workloads — Llama 3, DeepSeek, Mixtral, large embedding models — the H100 is roughly 2.0–2.5× faster than an A100 at FP16, and 3–4× faster when you can use FP8 (Hopper's Transformer Engine).
If your workload is bandwidth-bound (most LLM decode is), the 1.7× memory-bandwidth advantage shows up directly in tokens/sec.
Where the A100 wins
- Cost per token at FP16 is often a wash because the H100 costs ~2× to rent.
- Availability: A100s are everywhere; H100s still get queued during demand spikes.
- Mature tooling: every framework has been tuned for A100 for years.
Practical guidance
- Pick A100 for development, smaller fine-tunes, embedding pipelines, or when FP8 isn't yet supported in your stack.
- Pick H100 for high-throughput production inference, large-context generation, or any path where FP8 quantization is on the table.
Run the napkin math: H100 hourly cost ÷ tokens per hour. If FP8 is on the table, H100 usually wins on cost per million tokens despite the higher sticker price.