Local AI Advisor

Can Mac mini M6 24 GB run Qwen3 8B?

24 GB unified · 170 GB/s memory bandwidth · Qwen3 8B has 8.2B parameters.

Short answer

Yes, excellent for chat; borderline for agentic coding.

Best quantization for chat: 4-bit (4.4 GB of weights) on LM Studio. Generation: 16–25 tok/s, leaving about 10 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-bit16–25 tok/s≈1 s8KDetails
Coding questionswith ContinueComfortable5-bit13–20 tok/s≈6 s16KDetails
Coding in a repositorywith AiderAcceptable6-bit9–15 tok/s≈20 s32KDetails
Agentic codingwith OpenCodeBorderline6-bit8–14 tok/s≈30 s32KDetails
Long documents (64K)with Open WebUIAcceptable8-bit6–11 tok/s≈50 s32KDetails

Speeds are bandwidth-based estimates; no direct benchmark exists for this pair. “First reply” is the time to read the workload’s opening prompt. How we calculate

Get it running

Copy-paste commands for the 8-bit build with a 32K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3-8B-GGUF:Q8_0

Downloads 8.71 GB (Q8_0). 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.