Yes — with 128.9 GB to spare
Mistral Small 3.2 24B at Q4_K_M fits your M2 Ultra · 192 GB entirely in unified memory at 8K context, at an estimated 34 tokens per second. There is room for its full 128K window.
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 128.9 GB of 144.0 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 23.4 GB | 25.2 GB | 128K | 19 | −0.1% ppl | Long context |
| Q6_K | 18.0 GB | 19.9 GB | 128K | 25 | −0.4% ppl | Long context |
| Q5_K_M | 15.6 GB | 17.4 GB | 128K | 29 | −0.8% ppl | Long context |
| Q4_K_M | 13.3 GB | 15.1 GB | 128K | 34 | −1.9% ppl | Recommended |
| Q3_K_M | 10.7 GB | 12.6 GB | 128K | 42 | −5.4% ppl | Long context |
| Q2_K | 9.2 GB | 11.1 GB | 128K | 49 | −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 144.0 GB of 192 GB. Raising it with iogpu.wired_limit_mb is possible, and risky.
03There is room to go to the model's full 128K context on this card.