Yes — with 130.6 GB to spare
gpt-oss 120B at MXFP4 fits your M3 Ultra · 256 GB entirely in unified memory at 8K context, at an estimated 79 tokens per second. There is room for its full 128K window.
Fully in unified memory
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
Free 130.6 GB of 192.0 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| MXFP4 | 60.5 GB | 61.4 GB | 128K | 79 | Reference | Recommended |
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 192.0 GB of 256 GB. Raising it with iogpu.wired_limit_mb is possible, and risky.
03There is room to go to the model's full 128K context on this card.