Local AI Advisor

Can MacBook Pro M4 Max 48 GB run Qwen3 14B?

48 GB unified · 546 GB/s memory bandwidth · Qwen3 14B has 14.8B parameters.

Short answer

Yes, excellent for chat and acceptable or better for every workload we test.

Best quantization for chat: 5-bit (9.6 GB of weights) on LM Studio. Generation: 30–40 tok/s, leaving about 27 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 WebUIExcellent5-bit30–40 tok/s≈2 s8KDetails
Coding questionswith ContinueExcellent5-bit30–40 tok/s≈10 s16KDetails
Coding in a repositorywith AiderAcceptable6-bit20–30 tok/s≈40 s32KDetails
Agentic codingwith OpenCodeAcceptable6-bit20–30 tok/s≈60 s32KDetails
Long documents (64K)with Open WebUIAcceptable6-bit19–25 tok/s≈2 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 6-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:Q6_K

Downloads 12.12 GB (Q6_K). 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.