Yes — with 42.3 GB to spare
Mistral 7B Instruct v0.3 at Q4_K_M fits your M3 Max · 64 GB entirely in unified memory at 8K context, at an estimated 55 tokens per second. There is room for its full 32K window.
Fully in unified memory
8K context
Q4_K_M · 4.1 GB
Apache 2.0
Released May 2024
Old but extremely well behaved, and permissively licensed for commercial use.
The VRAM budget
weights 4.1 GB
Weights 4.1 GB
KV cache @ 8K 1.00 GB
Runtime overhead 0.6 GB
Free 42.3 GB of 48.0 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| F16 | 13.5 GB | 15.1 GB | 32K | 17 | Reference | Long context |
| Q8_0 | 7.2 GB | 8.8 GB | 32K | 31 | −0.1% ppl | Long context |
| Q6_K | 5.5 GB | 7.1 GB | 32K | 40 | −0.4% ppl | Long context |
| Q5_K_M | 4.8 GB | 6.4 GB | 32K | 47 | −0.8% ppl | Long context |
| Q4_K_M | 4.1 GB | 5.7 GB | 32K | 55 | −1.9% ppl | Recommended |
| Q3_K_M | 3.3 GB | 4.9 GB | 32K | 68 | −5.4% ppl | Long context |
| Q2_K | 2.8 GB | 4.4 GB | 32K | 79 | −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-7B-Instruct-v0.3-4bit \ --max-tokens 512 --prompt "Hello"
Apple's own array framework. The fastest path on Apple Silicon. More on MLX.
01Download is 4.1 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 48.0 GB of 64 GB. Raising it with iogpu.wired_limit_mb is possible, and risky.
03There is room to go to the model's full 32K context on this card.