text model · phi · macOS

Can I run Phi-4 14B on Apple M1 (8GB)?

Compatibility verdict VRAM threshold engine

No, not enough memorywould not load

No. Phi-4 14B needs ~10.8 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.

usable ~5.5 GB

Needs ~10.8 GB Device usable ~5.5 GB

Needs ~10.8 GB even at Q4_K_M, but only ~5.5 GB is usable.

Q4_K_M needed: ~10.8 GB
Usable on device: ~5.5 GB
Device memory: 8 GB

Model phi

Parameters: 14B
Q4_K_M size: 9.05 GB
Q8_0 size: 15.58 GB
Context: 16k
Ollama tag: phi4:14b

Full Phi-4 14B requirements →

Device macOS

Memory: 8 GB unified
Usable for weights: ~5.5 GB
Best runtime: Ollama (llama.cpp Metal backend)

Best models for Apple M1 (8GB) →

What you can run instead

Run Phi-4 14B on other hardware

Nvidia GeForce RTX 3060 (12GB)Nvidia GeForce RTX 4070 (12GB)Nvidia GeForce RTX 4060 Ti (16GB)Nvidia GeForce RTX 4080 (16GB)16GB RAM Laptop (CPU/iGPU only)iPad Pro M4 (16GB, 1TB/2TB config)

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.

Sources

Memory figures are estimates. See methodology.