Local AI Advisor

Can MacBook Pro M3 Max 96 GB run Qwen3-Coder 30B-A3B Instruct?

96 GB unified · 300 GB/s memory bandwidth · Qwen3-Coder 30B-A3B Instruct has 30.5B parameters (3.3B active per token).

Short answer

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

Best quantization for chat: 6-bit (23 GB of weights) on LM Studio. Generation: 50–65 tok/s, leaving about 61 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 WebUIExcellent6-bit50–65 tok/s≈1 s8KDetails
Coding questionswith ContinueExcellent6-bit45–60 tok/s≈5 s16KDetails
Coding in a repositorywith AiderComfortable6-bit35–45 tok/s≈15 s32KDetails
Agentic codingwith OpenCodeComfortable6-bit30–40 tok/s≈25 s32KDetails
Long documents (64K)with Open WebUIComfortable6-bit18–25 tok/s≈80 s64KDetails

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 64K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q6_K

Downloads 25.09 GB (Q6_K). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=65536 before `ollama serve` for long prompts.

Different tool, context size or workload?

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