No — not on this device

Qwen3 32B at Q4_K_M needs 21.0 GB against 10.7 GB usable, and the shortfall of 10.3 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 Q4_K_M · 18.4 GB Apache 2.0 Released Apr 2025

The classic 24 GB target, and still the strongest local translator under 70B. Qwen3.8 27B is smaller and better at everything else.

What hardware do I need for Qwen3 32B? →

Fits instead: Qwen3 14B (10.2 GB) · Qwen3 8B (6.3 GB)

The VRAM budget

weights 18.4 GB

Weights 18.4 GB KV cache @ 8K 2.00 GB Runtime overhead 0.6 GB Over budget 10.3 GB past 10.7 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
Q8_0	32.5 GB	35.1 GB	—	~1.2	−0.1% ppl	24.4 GB over
Q6_K	25.0 GB	27.6 GB	—	~1.5	−0.4% ppl	16.9 GB over
Q5_K_M	21.7 GB	24.3 GB	—	~1.8	−0.8% ppl	13.6 GB over
Q4_K_M	18.4 GB	21.0 GB	—	~2.1	−1.9% ppl	10.3 GB over
Q3_K_M	14.9 GB	17.5 GB	—	~2.5	−5.4% ppl	6.8 GB over
Q2_K	12.8 GB	15.4 GB	—	~3.0	−15% ppl	4.7 GB over

Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it.

How to run it

Ollama llama.cpp LM Studio MLX

terminal

$ pip install mlx-lm
$ mlx_lm.generate --model mlx-community/Qwen3-32B-4bit \
    --max-tokens 512 --prompt "Hello"

Apple's own array framework. The fastest path on Apple Silicon. More on MLX.

01Download is 18.4 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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