Yes — with 181.6 GB to spare
Phi-4 14B at Q4_K_M fits your M3 Ultra · 256 GB entirely in unified memory at 8K context, at an estimated 54 tokens per second. There is room for its full 16K window.
Fully in unified memory
8K context
Q4_K_M · 8.3 GB
MIT
Released Dec 2024
Trained heavily on synthetic reasoning data. Short 16K window is its main limitation.
The VRAM budget
weights 8.3 GB
Weights 8.3 GB
KV cache @ 8K 1.56 GB
Runtime overhead 0.6 GB
Free 181.6 GB of 192.0 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| F16 | 27.4 GB | 29.5 GB | 16K | 16 | Reference | Fits |
| Q8_0 | 14.5 GB | 16.7 GB | 16K | 31 | −0.1% ppl | Fits |
| Q6_K | 11.2 GB | 13.4 GB | 16K | 40 | −0.4% ppl | Fits |
| Q5_K_M | 9.7 GB | 11.9 GB | 16K | 46 | −0.8% ppl | Fits |
| Q4_K_M | 8.3 GB | 10.4 GB | 16K | 54 | −1.9% ppl | Recommended |
| Q3_K_M | 6.7 GB | 8.9 GB | 16K | 67 | −5.4% ppl | Fits |
| Q2_K | 5.7 GB | 7.9 GB | 16K | 78 | −15% ppl | Fits |
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/phi-4-4bit \ --max-tokens 512 --prompt "Hello"
Apple's own array framework. The fastest path on Apple Silicon. More on MLX.
01Download is 8.3 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 192.0 GB of 256 GB. Raising it with iogpu.wired_limit_mb is possible, and risky.
03There is room to go to the model's full 16K context on this card.