Local AI Advisor

Can Mac mini M2 24 GB run Qwen3.6 27B?

24 GB unified · 100 GB/s memory bandwidth · Qwen3.6 27B has 27.8B parameters.

Short answer

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

Best quantization for chat: 3-bit (11 GB of weights) on LM Studio. Generation: 5–7 tok/s, leaving about 3.3 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 WebUIAcceptable3-bit5–7 tok/s≈20 s8KDetails
Coding questionswith ContinueBorderline3-bit5–7 tok/s≈2 min16KDetails
Coding in a repositorywith AiderTechnically Runs3-bit4.9–7 tok/s≈6 min32KDetails
Agentic codingwith OpenCodeTechnically Runs3-bit4.8–6 tok/s≈9 min32KDetails
Long documents (64K)with Open WebUITechnically Runs3-bit4.1–6 tok/s≈26 min64KDetails

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 8K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3.6-27B-GGUF:Q3_K_M

Downloads 13.59 GB (Q3_K_M). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=8192 before `ollama serve` for long prompts.

Different tool, context size or workload?

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