text model · Phi-3.5 · macOS
Can I run Phi-3.5-mini 3.8B on Apple M5 Pro (48GB)?
Yes. Phi-3.5-mini 3.8B runs on Apple M5 Pro (48GB) at Q4_K_M (~3.7 GB of ~32 GB usable).
Runs at Q4_K_M using ~3.7 GB of ~32 GB usable. You have room for FP16 for higher quality.
- Q4_K_M needed
- ~3.7 GB
- Usable on device
- ~32 GB
- Device memory
- 48 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
ollama run phi3.5:3.8b llama-cli -hf bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M lms get bartowski/Phi-3.5-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
- 3.82B
- Q4_K_M size
- 2.39 GB
- Q8_0 size
- 4.06 GB
- Context
- 128k
- Ollama tag
- phi3.5:3.8b
- Memory
- 48 GB unified
- Usable for weights
- ~32 GB
- Best runtime
- MLX direct / Ollama (MLX backend)
You could also run
Run Phi-3.5-mini 3.8B on other hardware
FAQ
Can Apple M5 Pro (48GB) run Phi-3.5-mini 3.8B?
Yes. Phi-3.5-mini 3.8B runs on Apple M5 Pro (48GB) at Q4_K_M (~3.7 GB of ~32 GB usable).
How much memory does Phi-3.5-mini 3.8B need?
Apple M5 Pro (48GB) has room to spare. At Q4_K_M the weights are ~2.39 GB; with KV cache and runtime overhead, budget ~3.7 GB at a 4k context.
What is the best tool to run Phi-3.5-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.