text model · phi · macOS
Can I run Phi-4-mini 3.8B on Apple M5 Max (128GB)?
Yes. Phi-4-mini 3.8B runs on Apple M5 Max (128GB) at Q4_K_M (~3.8 GB of ~96 GB usable).
Runs at Q4_K_M using ~3.8 GB of ~96 GB usable. You have room for FP16 for higher quality.
- Q4_K_M needed
- ~3.8 GB
- Usable on device
- ~96 GB
- Device memory
- 128 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
ollama run phi4-mini:3.8b llama-cli -hf bartowski/microsoft_Phi-4-mini-instruct-GGUF:Q4_K_M lms get bartowski/microsoft_Phi-4-mini-instruct-GGUF 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.
- Parameters
- 3.8B
- Q4_K_M size
- 2.49 GB
- Q8_0 size
- 4.08 GB
- Context
- 128k
- Ollama tag
- phi4-mini:3.8b
- Memory
- 128 GB unified
- Usable for weights
- ~96 GB
- Best runtime
- MLX direct / Ollama (MLX backend)
You could also run
Run Phi-4-mini 3.8B on other hardware
FAQ
Can Apple M5 Max (128GB) run Phi-4-mini 3.8B?
Yes. Phi-4-mini 3.8B runs on Apple M5 Max (128GB) at Q4_K_M (~3.8 GB of ~96 GB usable).
How much memory does Phi-4-mini 3.8B need?
Apple M5 Max (128GB) has room to spare. At Q4_K_M the weights are ~2.49 GB; with KV cache and runtime overhead, budget ~3.8 GB at a 4k context.
What is the best tool to run Phi-4-mini 3.8B 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.