Yes — with 7.0 GB to spare
Olmo 3.1 32B Instruct at Q4_K_M fits your M3 Pro · 36 GB entirely in unified memory at 8K context, at an estimated 4.6 tokens per second. There is room for its full 64K window.
Fully in unified memory
8K context
Q4_K_M · 18.1 GB
Apache 2.0
Released 10 Dec 2025
The largest fully open model you can audit end to end. Q4 fits 24 GB, tightly.
The VRAM budget
weights 18.1 GB
Weights 18.1 GB
KV cache @ 8K 1.25 GB
Runtime overhead 0.6 GB
Free 7.0 GB of 27.0 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 31.9 GB | 33.7 GB | — | ~2.6 | −0.1% ppl | 6.7 GB over |
| Q6_K | 24.6 GB | 26.4 GB | 16K | 3.4 | −0.4% ppl | Fits |
| Q5_K_M | 21.3 GB | 23.1 GB | 64K | 3.9 | −0.8% ppl | Long context |
| Q4_K_M | 18.1 GB | 20.0 GB | 64K | 4.6 | −1.9% ppl | Recommended |
| Q3_K_M | 14.7 GB | 16.5 GB | 64K | 5.7 | −5.4% ppl | Long context |
| Q2_K | 12.6 GB | 14.4 GB | 64K | 6.7 | −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. This model interleaves sliding-window layers (4096 tokens, 16 of 64 layers global), which is why its cache barely grows with context.
How to run it
$ pip install mlx-lm $ mlx_lm.generate --model mlx-community/Olmo-3.1-32B-Instruct-4bit \ --max-tokens 512 --prompt "Hello"
Apple's own array framework. The fastest path on Apple Silicon. More on MLX.
01Download is 18.1 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 27.0 GB of 36 GB. Raising it with iogpu.wired_limit_mb is possible, and risky.
03There is room to go to the model's full 64K context on this card.