Yes — with 36.4 GB to spare
gpt-oss 20B at MXFP4 fits your M4 Max · 64 GB entirely in unified memory at 8K context, at an estimated 57 tokens per second. There is room for its full 128K window.
Fully in unified memory
8K context
MXFP4 · 10.8 GB
Apache 2.0
Released Aug 2025
Native MXFP4
Ships natively in MXFP4, so the 4-bit weights are the reference weights, not a lossy copy. Fits 16 GB.
The VRAM budget
weights 10.8 GB
Weights 10.8 GB
KV cache @ 8K 0.19 GB
Runtime overhead 0.6 GB
Free 36.4 GB of 48.0 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| MXFP4 | 10.8 GB | 11.6 GB | 128K | 57 | 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-20b-4bit \ --max-tokens 512 --prompt "Hello"
Apple's own array framework. The fastest path on Apple Silicon. More on MLX.
01Download is 10.8 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 48.0 GB of 64 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.