Yes — with 53.2 GB to spare
Llama 3.3 70B Instruct at Q4_K_M fits your M3 Max · 128 GB entirely in unified memory at 8K context, at an estimated 5.6 tokens per second. There is room for its full 128K window.
Fully in unified memory
8K context
Q4_K_M · 39.7 GB
Llama 3.3 Community
Released Dec 2024
Still the creative-writing favourite: consistent voice, takes direction. Needs 48 GB to sit comfortably on GPU at Q4.
The VRAM budget
weights 39.7 GB
Weights 39.7 GB
KV cache @ 8K 2.50 GB
Runtime overhead 0.6 GB
Free 53.2 GB of 96.0 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 69.9 GB | 73.0 GB | 81K | 3.2 | −0.1% ppl | Long context |
| Q6_K | 53.9 GB | 57.0 GB | 128K | 4.1 | −0.4% ppl | Long context |
| Q5_K_M | 46.6 GB | 49.7 GB | 128K | 4.8 | −0.8% ppl | Long context |
| Q4_K_M | 39.7 GB | 42.8 GB | 128K | 5.6 | −1.9% ppl | Recommended |
| Q3_K_M | 32.1 GB | 35.2 GB | 128K | 7.0 | −5.4% ppl | Long context |
| Q2_K | 27.5 GB | 30.6 GB | 128K | 8.1 | −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/Llama-3.3-70B-Instruct-4bit \ --max-tokens 512 --prompt "Hello"
Apple's own array framework. The fastest path on Apple Silicon. More on MLX.
01Download is 39.7 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 96.0 GB of 128 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.