A100 80GB LLM Calculator — What Can It Run?
Check which models fit on a A100 80GB: max parameters per precision, cache headroom and estimated tokens/sec.
A100 80GB: 80 GB, 2039 GB/s, ~312 TFLOPS FP16. The A100 80GB defined the LLM era: 2 TB/s HBM2e and 312 TFLOPS BF16. One card serves 70B at 4–8 bit; eight of them trained most of the 2022–23 generation of models.
Formula
About A100 80GB LLM Calculator — What Can It Run?
"Will it run?" is the first question of local AI, and for the A100 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 2039 GB/s is the speed limit that matters). The A100 80GB defined the LLM era: 2 TB/s HBM2e and 312 TFLOPS BF16. One card serves 70B at 4–8 bit; eight of them trained most of the 2022–23 generation of models.
How to use A100 80GB LLM Calculator — What Can It Run?
- 1Enter your values into A100 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 A100 80GB LLM Calculator — What Can It Run??
- ✓Computes A100 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 does one A100 80GB serve in production?+
Llama-3-70B at INT8/FP8 (~71 GB) just fits for short contexts; at 4-bit (~38 GB) it serves with large batch room. It is also the classic host for FP16 13–34B models at high concurrency, where its bandwidth keeps batched decode fast.
A100 vs H100 — when is the older card the right rental?+
When you are memory-capacity-bound, not compute-bound: serving quantized models, embedding generation, fine-tuning with QLoRA. A100s rent at a steep discount, and decode-heavy workloads see far less than the 3× headline H100 speedup.
How is the tokens/sec estimate for the A100 80GB derived?+
Decode is memory-bound: each token reads every weight once, so speed ≈ effective bandwidth ÷ model size. We assume ~60% of the 2039 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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