text model · phi · macOS
Can I run Phi-4 14B on Apple M1 (8GB)?
No. Phi-4 14B needs ~10.8 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.
Needs ~10.8 GB even at Q4_K_M, but only ~5.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 5.3 GB: Phi-4 14B needs roughly 10.8 GB at Q4_K_M and Apple M1 (8GB) leaves only about 5.5 GB usable for a model. The lightest tracked hardware that runs Phi-4 14B is the Nvidia GeForce RTX 3060 (12GB) at 12 GB. See Phi-4 14B on Nvidia GeForce RTX 3060 (12GB).
- Q4_K_M needed
- ~10.8 GB
- Usable on device
- ~5.5 GB
- Device memory
- 8 GB
Which quant fits
- Parameters
- 14B
- Q4_K_M size
- 9.05 GB
- Q8_0 size
- 15.58 GB
- Context
- 16k
- Ollama tag
- phi4:14b
- Memory
- 8 GB unified
- Usable for weights
- ~5.5 GB
- Power draw
- ~39 W
- Best runtime
- Ollama (llama.cpp Metal backend)
What you can run instead
Run Phi-4 14B on other hardware
FAQ
Can Apple M1 (8GB) run Phi-4 14B?
No. Phi-4 14B needs ~10.8 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.
How much memory does Phi-4 14B need?
Apple M1 (8GB) does not have enough memory. At Q4_K_M the weights are ~9.05 GB; with KV cache and runtime overhead, budget ~10.8 GB at a 4k context.
What is the best tool to run Phi-4 14B on macOS?
LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.
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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/phi-4-14b/apple-m1-8gb) Sources
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.