Can H100 80GB SXM5 run gpt-oss-120b?

NVIDIA · 80 GB HBM3 · 3350 GB/s bandwidth · OpenAI · 120.4B (active 5.1B) · MoE · model ctx up to 128K

Fit & speed by quant and context

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).

VRAM breakdown at 8K context

Other GPUs that run gpt-oss-120b

• B200 192GB SXM — ≈998.2 tok/s Q4 est.
• MI300X 192GB — ≈661.3 tok/s Q4 est.
• H200 141GB SXM5 — ≈598.9 tok/s Q4 est.
• Gaudi 3 128GB — ≈408.8 tok/s Q4 est.
• A100 80GB SXM4 — ≈254.4 tok/s Q4 est.
• Mac Studio M3 Ultra 96GB — ≈102.2 tok/s Q4 est.
• Mac Studio M2 Ultra 192GB — ≈79.7 tok/s Q4 measured
• MacBook Pro M4 Max 128GB — ≈68.1 tok/s Q4 est.

All GPUs for gpt-oss-120b →

Other models for the H100 80GB SXM5

• MiniCPM5-1B — ≈1973.9 tok/s Q4 est.
• Qwen3.5-2B — ≈1065.9 tok/s Q4 est.
• DeepSeek-R1-0528-Qwen3-8B-layer-mix-bpw-3.8-mlx — ≈926.9 tok/s Q4 est.
• Qwen3.6-35B-A3B — ≈710.6 tok/s Q4 est.
• Qwen3.5-35B-A3B — ≈710.6 tok/s Q4 est.
• GLM-4.7-Flash — ≈710.6 tok/s Q4 est.
• Qwen3-30B-A3B-Instruct-2507 — ≈710.6 tok/s Q4 est.
• Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 — ≈710.6 tok/s Q4 est.

All models on the H100 80GB SXM5 →