No — not on this device

gpt-oss 20B at MXFP4 needs 11.6 GB against 10.7 GB usable, and the shortfall of 0.9 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 · 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 Over budget 0.9 GB past 10.7 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
MXFP4	10.8 GB	11.6 GB	—	~9.5	Reference	0.9 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

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 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.

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