NVIDIA · 80 GB HBM3 · 3350 GB/s bandwidth ·
OpenAI · 21.5B (active 3.6B) · MoE · model ctx up to 128K
Yes — the H100 80GB SXM5 (80 GB HBM3) can run gpt-oss-20b. At Q4 with the default 8K context it needs ≈ 13.55 GB VRAM (weights 11.83 GB + KV cache 0.72 GB + 1 GB runtime overhead), which fits within 80 GB. Decode at Q4/8K: ≈ 592.2 tok/s (estimated, batch 1). FP16 (≈ 44.72 GB) also fits, at ≈ 162.8 tok/s (estimated). Estimates come from memory-bandwidth math; rows tagged measured override estimates. Relative ranking is more reliable than absolute tok/s. Methodology.
Fit & speed by quant and context
Quant
Context
VRAM needed
Fits?
tok/s (decode)
Q4
4K
13.19 GB
Yes
592.2est.
Q4
8K default
13.55 GB
Yes
592.2est.
Q4
32K
15.71 GB
Yes
592.2est.
FP16
4K
44.36 GB
Yes
162.8est.
FP16
8K default
44.72 GB
Yes
162.8est.
FP16
32K
46.88 GB
Yes
162.8est.
Fits = weights + KV(ctx) + 1 GB overhead ≤ 95% of VRAM. Measured
anchors are context-agnostic; the fit verdict is recomputed per context. A missing tok/s
means the model is far beyond this card (offload-only territory).