Yes, just — 3.0 GB spare

Qwen3 32B at Q4_K_M fits your M2 Pro · 32 GB entirely in unified memory at 8K context, at an estimated 6.1 tokens per second. Past 19K the KV cache pushes it over — quantise the cache to q8_0, or step down a quantisation, to go longer.

Fully in unified memory 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? →

The VRAM budget

weights 18.4 GB

Weights 18.4 GB KV cache @ 8K 2.00 GB Runtime overhead 0.6 GB Free 3.0 GB of 24.0 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
Q8_0	32.5 GB	35.1 GB	—	~3.4	−0.1% ppl	11.1 GB over
Q6_K	25.0 GB	27.6 GB	—	~4.5	−0.4% ppl	3.6 GB over
Q5_K_M	21.7 GB	24.3 GB	6K	~5.2	−0.8% ppl	0.3 GB over
Q4_K_M	18.4 GB	21.0 GB	19K	6.1	−1.9% ppl	Recommended
Q3_K_M	14.9 GB	17.5 GB	33K	7.5	−5.4% ppl	Long context
Q2_K	12.8 GB	15.4 GB	42K	8.7	−15% ppl	Long context

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 24.0 GB of 32 GB. Raising it with iogpu.wired_limit_mb is possible, and risky.

03Only 3.0 GB is spare, so a long system prompt can still push it over. Its real ceiling here is 19K context.

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