Local AI Advisor

Can Mac mini M4 Pro 64 GB run Qwen3.8 27B?

64 GB unified · 273 GB/s memory bandwidth · Qwen3.8 27B has 27.8B parameters.

Short answer

Yes, comfortable for chat; borderline for coding in a repository.

Best quantization for chat: 4-bit (15 GB of weights) on LM Studio. Generation: 11–14 tok/s, leaving about 37 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 WebUIComfortable4-bit11–14 tok/s≈7 s8KDetails
Coding questionswith ContinueAcceptable4-bit11–14 tok/s≈45 s16KDetails
Coding in a repositorywith AiderBorderline4-bit10–14 tok/s≈2 min32KDetails
Agentic codingwith OpenCodeBorderline5-bit8–11 tok/s≈3 min32KDetails
Long documents (64K)with Open WebUIBorderline5-bit7–10 tok/s≈9 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 4-bit build with a 16K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_XL

Downloads 17.56 GB (UD-Q4_K_XL). 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.