Yes — with 8.9 GB to spare
Mistral Small 3.2 24B at Q4_K_M fits your M4 · 32 GB entirely in unified memory at 8K context, at an estimated 5.1 tokens per second. Past 64K 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 · 13.3 GB
Apache 2.0
Released Jun 2025
Vision
Apache-2.0, vision-capable, and the most 24 GB-friendly of the 2025 generalists.
The VRAM budget
weights 13.3 GB
Weights 13.3 GB
KV cache @ 8K 1.25 GB
Runtime overhead 0.6 GB
Free 8.9 GB of 24.0 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 23.4 GB | 25.2 GB | — | ~2.9 | −0.1% ppl | 1.2 GB over |
| Q6_K | 18.0 GB | 19.9 GB | 34K | 3.7 | −0.4% ppl | Long context |
| Q5_K_M | 15.6 GB | 17.4 GB | 50K | 4.3 | −0.8% ppl | Long context |
| Q4_K_M | 13.3 GB | 15.1 GB | 64K | 5.1 | −1.9% ppl | Recommended |
| Q3_K_M | 10.7 GB | 12.6 GB | 81K | 6.2 | −5.4% ppl | Long context |
| Q2_K | 9.2 GB | 11.1 GB | 90K | 7.3 | −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
$ pip install mlx-lm $ mlx_lm.generate --model mlx-community/Mistral-Small-3.2-24B-Instruct-2506-4bit \ --max-tokens 512 --prompt "Hello"
Apple's own array framework. The fastest path on Apple Silicon. More on MLX.
01Download is 13.3 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.
03There is room to go to 64K context on this card.