No — not on this device
gpt-oss 120B at MXFP4 needs 61.4 GB against 10.7 GB usable, and the shortfall of 50.7 GB is more than 32 GB of system RAM can cover at a tolerable speed. A smaller sibling or a lower quantisation is the honest answer here.
Does not fit
8K context
MXFP4 · 60.5 GB
Apache 2.0
Released Aug 2025
Native MXFP4
Designed to land on one 80 GB card. Only ~5B parameters are active per token.
The VRAM budget
weights 60.5 GB
Weights 60.5 GB
KV cache @ 8K 0.29 GB
Runtime overhead 0.6 GB
Over budget 50.7 GB past 10.7 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| MXFP4 | 60.5 GB | 61.4 GB | — | ~6.7 | Reference | 50.7 GB over |
Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it. This model interleaves sliding-window layers (128 tokens, 1 global in 2), which is why its cache barely grows with context.
How to run it
$ pip install mlx-lm $ mlx_lm.generate --model mlx-community/gpt-oss-120b-4bit \ --max-tokens 512 --prompt "Hello"
Apple's own array framework. The fastest path on Apple Silicon. More on MLX.
01Download is 60.5 GB. Keep it on an SSD — a first load off a spinning disk takes minutes.
02macOS caps what the GPU may wire down at about 10.7 GB of 16 GB. Raising it with iogpu.wired_limit_mb is possible, and risky.
03If it falls back to CPU silently, drop the context first, then step down a quantisation.