H100 80GB LLM Calculator — What Can It Run?
Check which models fit on a H100 80GB: max parameters per precision, cache headroom and estimated tokens/sec.
H100 80GB: 80 GB, 3350 GB/s, ~989 TFLOPS FP16. The H100 added FP8 tensor cores (989 dense BF16 TFLOPS, ~2× that in FP8) and 3.35 TB/s HBM3 — the card the 2023–25 frontier was trained and served on, and the unit in which AI compute is now priced.
Formula
About H100 80GB LLM Calculator — What Can It Run?
"Will it run?" is the first question of local AI, and for the H100 80GB this calculator answers it precisely: enter any model's parameter count and quantization and get the memory bill against this card's 80 GB, the largest model it can hold at that quant, and a bandwidth-derived decode-speed estimate (token generation streams the whole model per token, so 3350 GB/s is the speed limit that matters). The H100 added FP8 tensor cores (989 dense BF16 TFLOPS, ~2× that in FP8) and 3.35 TB/s HBM3 — the card the 2023–25 frontier was trained and served on, and the unit in which AI compute is now priced.
How to use H100 80GB LLM Calculator — What Can It Run?
- 1Enter your values into H100 80GB LLM Calculator — What Can It Run? — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use H100 80GB LLM Calculator — What Can It Run??
- ✓Computes H100 80GB LLM instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: needed = params × bpw ÷ 8 + reserve.
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
What makes H100 worth 2–3× the A100's price?+
Training: ~3× BF16 compute plus FP8 (≈2× again) with Transformer Engine — large-run cost per token drops despite the premium. Serving: 64% more bandwidth speeds decode, and FP8 weights double effective capacity. For light inference, the premium often isn't justified.
How many H100s for Llama-3-70B at full precision?+
BF16 weights are ~141 GB — two H100s minimum with tensor parallelism, leaving ~19 GB across the pair for cache (tight); four is the comfortable production setup. In FP8 it serves on a single card (~71 GB) for moderate contexts.
How is the tokens/sec estimate for the H100 80GB derived?+
Decode is memory-bound: each token reads every weight once, so speed ≈ effective bandwidth ÷ model size. We assume ~60% of the 3350 GB/s peak is achievable, matching llama.cpp benchmarks within ~20%. Prompt prefill is compute-bound and much faster per token.
Why reserve memory beyond the weights?+
The KV cache grows with context (use our per-model KV-cache calculators), CUDA/Metal runtimes take hundreds of MB, and allocator fragmentation wastes more. The default reserve suits 2–8K contexts; long-context work needs significantly more.
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