What LLMs can the A100 80GB (SXM) run?

61 of the 62 open models in this roster have an on-device configuration with usable context. The largest by exact parameter count is Laguna S 2.1 at Q4_K_M, with room for 143k tokens. “Largest” describes parameter count, not model quality or task performance.

The device publishes 80 GB of HBM2e. This calculator budgets 92%, or 73.6 GiB, for model weights and KV cache; the remainder is an explicit allowance for the runtime, driver or operating system, workspace, and display. It is an assumption rather than a vendor specification.

No. They are bandwidth-bound roofline ceilings: 2039 GB/s divided by the bytes one decoded token actually reads, which the table prints beside every ceiling. That is not the size of the file on disk — a token reads the weights it routes through, all of them for a dense model but only the selected experts for a mixture-of-experts, plus one pass over the KV cache. gpt-oss 120B holds 60.8 GiB resident at As released yet reads 3.3 GiB/token, which is what produces its 583 tok/s. Real throughput is lower because kernels, cache traffic, prompt processing, scheduling, and runtime overhead also consume time.