Yes — with 36.4 GB to spare

gpt-oss 20B at MXFP4 fits your M3 Max · 64 GB entirely in unified memory at 8K context, at an estimated 56 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.

What hardware do I need for gpt-oss 20B? →

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	56	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

Ollama llama.cpp LM Studio MLX

terminal

$ 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.

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