No — not on this device
Mistral Small 3.2 24B at Q4_K_M needs 15.1 GB against 10.7 GB usable, and the shortfall of 4.4 GB is more than 256 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 · 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
Over budget 4.4 GB past 10.7 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 23.4 GB | 25.2 GB | — | ~1.6 | −0.1% ppl | 14.5 GB over |
| Q6_K | 18.0 GB | 19.9 GB | — | ~2.1 | −0.4% ppl | 9.2 GB over |
| Q5_K_M | 15.6 GB | 17.4 GB | — | ~2.4 | −0.8% ppl | 6.7 GB over |
| Q4_K_M | 13.3 GB | 15.1 GB | — | ~2.9 | −1.9% ppl | 4.4 GB over |
| Q3_K_M | 10.7 GB | 12.6 GB | — | ~3.5 | −5.4% ppl | 1.9 GB over |
| Q2_K | 9.2 GB | 11.1 GB | 5K | ~4.1 | −15% ppl | 0.4 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
$ 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 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.