Local AI Advisor

Can MacBook Pro M1 Pro 32 GB run Phi-4 (14B)?

32 GB unified · 200 GB/s memory bandwidth · Phi-4 (14B) has 14.7B parameters.

Short answer

Yes, comfortable for chat; borderline for agentic coding.

Best quantization for chat: 5-bit (9.5 GB of weights) on LM Studio. Generation: 12–16 tok/s, leaving about 12 GB free for your other apps.

How it rates for each workload

Same model and machine, different jobs. Agents and long documents send far bigger prompts than chat, so they need much more speed and memory.

WorkloadVerdictQuantGenerationFirst replyContext
Chatwith Open WebUIComfortable5-bit12–16 tok/s≈6 s8KDetails
Coding questionswith ContinueAcceptable5-bit11–15 tok/s≈40 s16KDetails
Coding in a repositorywith AiderAcceptable4-bit11–15 tok/s≈2 min16KDetails
Agentic codingwith OpenCodeBorderline6-bit8–11 tok/s≈2 min16KDetails
Long documents (64K)with Open WebUIBorderline6-bit8–11 tok/s≈2 min16KDetails

Speeds are estimates calibrated against measured benchmarks on similar hardware. “First reply” is the time to read the workload’s opening prompt. How we calculate

Get it running

Copy-paste commands for the 4-bit build with a 16K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/phi-4-GGUF:Q4_K_M

Downloads 8.89 GB (Q4_K_M). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=16384 before `ollama serve` for long prompts.

Different tool, context size or workload?

These pages use typical settings. The full check models your exact setup.