Can the NVIDIA H100 80GB PCIe run gpt-oss-120b?
IT FITS
Yes. 4 of 9 formats evaluated fit in 80 GB, at 8,192 tokens. No published file exists for this pair, so every size is rebuilt from the pinned architecture. The best quality that fits is Q4_K_M, needing 71.9 GB and running at an estimated 383 tokens per second.
Fitting in memory is not the same as loading. Whether the runtime and version you have supports this architecture and format on this machine has not been tested here.
How long a conversation. At Q4_K_M, this pair holds 131,072 tokens of context — roughly 98,304 words — needing 76.5 GB. That is OpenAI's own configured maximum of 131,072 tokens, not the card running out. A larger device does not extend it; a documented RoPE or YaRN extension might, and is a separate question from this one.
Every format evaluated
No published file exists for this pair, so every size is rebuilt from the pinned architecture. The Basis column says which is which for each row. Decode and first-token figures are calibrated estimates, not runs on this card.
| Format | Needs | Of 80 GB | Fits | Decode | First token | Basis |
|---|---|---|---|---|---|---|
| FP16 | 234.7 GB | 293% | short by 154.7 GB | does not run | — | reconstructed size |
| Q8_0 | 125.2 GB | 157% | short by 45.2 GB | does not run | — | reconstructed size |
| Q6_K | 97.0 GB | 121% | short by 17.0 GB | does not run | — | reconstructed size |
| Q5_K_M | 84.1 GB | 105% | short by 4.07 GB | does not run | — | reconstructed size |
| Q5_0 | 81.5 GB | 102% | short by 1.53 GB | does not run | — | reconstructed size |
| Q4_K_M | 71.9 GB | 90% | yes | ~383 tok/s | no comparable peak | reconstructed size |
| Q4_0 | 67.0 GB | 84% | yes | ~401 tok/s | no comparable peak | reconstructed size |
| Q3_K_M | 59.0 GB | 74% | yes | ~448 tok/s | no comparable peak | reconstructed size |
| Q2_K | 43.7 GB | 55% | yes | ~554 tok/s | no comparable peak | reconstructed size |
Sized at 8,192 tokens of context with the whole model resident — weights, the KV cache and the runtime reserve, offload off. Speed is only quoted for a format that fits: a rate for a configuration that cannot load is not a fact about anything.
Why the first-token figure is the same on every row
First token is modelled from the arithmetic the prompt requires, and that count does not change with the weight format — which is why it reads the same on every row. Real prefill does vary by format, because a quantized matmul is a different kernel; this model does not capture that, and the figure should be read as an order of magnitude rather than a ranking between formats.
What the labels mean
- published data
- Read from a published source — a file's byte count, a model's configuration or a manufacturer's specification — or exact arithmetic on such values. Not a measurement on a machine.
- reconstructed size
- Weight size reconstructed from the pinned architecture, because no published file exists.
- size range
- Only a lower and an upper bound are claimed for this weight size.
- estimate
- Calculated from sourced inputs by a stated method; an estimate, not a measurement.
This page fixes the context at 8,192 tokens and one device. Batch, concurrent users, KV-cache format, clusters and rental cost are all in the calculator, already set to this pairing.
Open in the calculator →Everything the NVIDIA H100 80GB PCIe runs
gpt-oss-120b’s architecture is read from its publisher’s own config.json at a pinned revision, and the NVIDIA H100 80GB PCIe’s 80 GB and 2000 GB/s come from the manufacturer’s specification. How far each figure can be trusted is published, per format and worst case included, on the accuracy scorecard.
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