Local AI Advisor

Can MacBook Pro M2 Pro 32 GB run Qwen3 32B?

32 GB unified · 200 GB/s memory bandwidth · Qwen3 32B has 32.8B parameters.

Short answer

Yes, acceptable for chat; not recommended for coding in a repository.

Best quantization for chat: 4-bit (17 GB of weights) on LM Studio. Generation: 7–9 tok/s, leaving about 3.8 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 WebUIAcceptable4-bit7–9 tok/s≈10 s8KDetails
Coding questionswith ContinueAcceptable3-bit8–11 tok/s≈80 s16KDetails
Coding in a repositorywith AiderTechnically Runs3-bit7–9 tok/s≈5 min32KDetails
Agentic codingwith OpenCodeTechnically Runs3-bit6–8 tok/s≈7 min32KDetails
Long documents (64K)with Open WebUIBorderline3-bit6–8 tok/s≈11 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 3-bit build with a 16K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3-32B-GGUF:Q3_K_M

Downloads 15.97 GB (Q3_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.