Can the Apple M1 (16GB) run Phi-4 14B?
Yes, with heavy quantization
QuantQ3_K_MNoticeably degraded
Memory9.5 GBof 10.7 GB
Speed~6tokens/s
Only just. Phi-4 14B fits on the Apple M1 (16GB) at Q3_K_M, which costs noticeable quality. Expect about 6 tokens/second. A card with more memory is a better experience.
Try other context lengths in the calculator →Run it in the cloud
Want full quality? Run Phi-4 14B on a bigger rented GPU.
Rent a cloud GPU by the hour. No upfront cost, cancel any time.
Memory needed by quantization
At 8,192 tokens of context. macOS lets the GPU use about 10.7 GB of the 16 GB unified memory by default.
| Quant | Quality | Memory | Fits? | Speed |
|---|---|---|---|---|
| FP16 | Full quality | 31.7 GB | Too big | — |
| Q8_0 | Near-lossless | 17.9 GB | Too big | — |
| Q6_K | Excellent | 14.3 GB | Too big | — |
| Q5_K_M | Very good | 12.7 GB | Too big | — |
| Q4_K_M | Good, most popular | 11.2 GB | Too big | — |
| Q3_K_M | Noticeably degraded | 9.5 GB | Fits | ~6 tok/s |
| Q2_K | Heavily degraded | 7.8 GB | Fits | ~7 tok/s |
How to run Phi-4 14B on the Apple M1 (16GB)
Ollama's default phi4:14b download is Q4_K_M, which needs about 11.2 GB, more than the 10.7 GB available. Download the Q3_K_M GGUF instead (LM Studio lists quants per model), or try a smaller context length.
GPUs that run Phi-4 14B comfortably
- NVIDIA RTX 3060 12GB
- NVIDIA RTX 4070 (12 GB)
- NVIDIA RTX 5070 (12 GB)
- NVIDIA RTX 4060 Ti 16GB
- NVIDIA RTX 5060 Ti 16GB