A100 80GB vs H100 80GB SXM for Local LLMs
Both have 80.0 GB, so they run the same models. The H100 80GB SXM generates tokens about 57% faster. Below: speeds on seven popular models, the models only one card can run, and our verdict.
Specs that matter for AI
| A100 80GB | H100 80GB SXM | |
|---|---|---|
| Memory | 80 GB | 80 GB |
| GPU-usable memory | 80.0 GB | 80.0 GB |
| Bandwidth | 2039 GB/s | 3350 GB/s |
| Architecture | Ampere | Hopper |
| Released | 2020-11 | 2022-09 |
| Llama 3.1 8B Q4_K_M | ~227 tok/s | ~357 tok/s |
Speed and fit by model
Q4_K_M (or native format) with 8K context.
| Model | VRAM | A100 80GB | H100 80GB SXM |
|---|---|---|---|
| Llama 3.1 8B | 6.3 GB | Fits ~227 tok/s | Fits ~357 tok/s |
| Qwen 3 14B | 10.5 GB | Fits ~130 tok/s | Fits ~207 tok/s |
| gpt-oss-20b | 13.9 GB | Fits ~234 tok/s | Fits ~283 tok/s |
| Qwen 3 30B-A3B | 18.8 GB | Fits ~151 tok/s | Fits ~169 tok/s |
| Qwen 3 32B | 21.4 GB | Fits ~61 tok/s | Fits ~97 tok/s |
| Llama 3.3 70B | 43.2 GB | Fits ~29 tok/s | Fits ~47 tok/s |
| gpt-oss-120b | 62.0 GB | Fits ~170 tok/s | Fits ~201 tok/s |
Verdict
Same 80 GB capacity. The H100 SXM has about 1.6 times the bandwidth and native FP8, so it serves the same model with much higher throughput. When renting, the A100 can still be the cheaper option per token for small batches.
Which should you buy?
Choose the A100 80GB if…
- You rent and the hourly price is much lower.
- You serve small batches where bandwidth is not the bottleneck.
- Your software stack does not use FP8.
Choose the H100 80GB SXM if…
- You serve many users and want throughput.
- You use FP8 models or KV caches.
- You care about cost per token rather than cost per hour.
Best quality and longest context on each card
The highest-quality quantization that fits fully at 8K context, and the longest context that fits at Q4_K_M with an FP16 cache.
| Model | A100 80GB: best quant | H100 80GB SXM: best quant | A100 80GB: max context | H100 80GB SXM: max context |
|---|---|---|---|---|
| Qwen 3.5 9B | FP16 / BF16 | FP16 / BF16 | 256K | 256K |
| Qwen 3 14B | FP16 / BF16 | FP16 / BF16 | 40K | 40K |
| gpt-oss-20b | MXFP4 (official) | MXFP4 (official) | 128K | 128K |
| Qwen 3.6 27B | FP16 / BF16 | FP16 / BF16 | 256K | 256K |
| Qwen 3 32B | FP16 / BF16 | FP16 / BF16 | 40K | 40K |
| Llama 3.3 70B | Q8_0 | Q8_0 | 110K | 110K |
Image generation
Best weight precision for image models with model swapping, at each model's default resolution. Image generation is also limited by compute, so the card with more tensor-core throughput finishes each image faster even when both fit.
| Model | A100 80GB | H100 80GB SXM |
|---|---|---|
| SDXL 1.0 | FP16 / BF16 | FP16 / BF16 |
| FLUX.1 [dev] | FP16 / BF16 | FP16 / BF16 |
| Qwen-Image | FP16 / BF16 | FP16 / BF16 |
| Wan 2.2 A14B (T2V / I2V) | FP16 / BF16 | FP16 / BF16 |
Frequently asked questions
Is the A100 80GB or H100 80GB SXM better for local LLMs?
Same 80 GB capacity. The H100 SXM has about 1.6 times the bandwidth and native FP8, so it serves the same model with much higher throughput. When renting, the A100 can still be the cheaper option per token for small batches.
Which is faster for LLM inference?
The H100 80GB SXM, by about 57% for token generation on models that fit both (about 357 vs 227 tokens/s on Llama 3.1 8B Q4_K_M).
Which can run bigger models?
Neither: both have 80.0 GB usable, so they run the same models.