Local AI Advisor

Can Mac mini M4 Pro 24 GB run Qwen3 14B?

24 GB unified · 273 GB/s memory bandwidth · Qwen3 14B has 14.8B parameters.

Short answer

Yes, excellent for chat; borderline for coding in a repository.

Best quantization for chat: 4-bit (7.8 GB of weights) on LM Studio. Generation: 19–25 tok/s, leaving about 6.4 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 WebUIExcellent4-bit19–25 tok/s≈4 s8KDetails
Coding questionswith ContinueComfortable4-bit18–25 tok/s≈25 s16KDetails
Coding in a repositorywith AiderBorderline4-bit15–20 tok/s≈85 s32KDetails
Agentic codingwith OpenCodeBorderline4-bit14–19 tok/s≈2 min32KDetails
Long documents (64K)with Open WebUIAcceptable4-bit13–17 tok/s≈3 min32KDetails

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 32K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3-14B-GGUF:Q4_K_M

Downloads 9 GB (Q4_K_M). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=32768 before `ollama serve` for long prompts.

Different tool, context size or workload?

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