Local AI Advisor

Can Mac Studio M5 Ultra 512 GB run Qwen3.6 27B?

512 GB unified · 1228 GB/s memory bandwidth · Qwen3.6 27B has 27.8B parameters.

Short answer

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

Best quantization for chat: 5-bit (18 GB of weights) on LM Studio. Generation: 25–40 tok/s, leaving about 482 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 WebUIExcellent5-bit25–40 tok/sunder 1 s8KDetails
Coding questionswith ContinueExcellent4-bit30–50 tok/s≈3 s16KDetails
Coding in a repositorywith AiderExcellent4-bit30–45 tok/s≈10 s32KDetails
Agentic codingwith OpenCodeComfortable5-bit25–40 tok/s≈15 s32KDetails
Long documents (64K)with Open WebUIExcellent5-bit20–35 tok/s≈40 s64KDetails

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 5-bit build with a 64K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3.6-27B-GGUF:Q5_K_M

Downloads 19.51 GB (Q5_K_M). 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.