Local AI Advisor

Can MacBook Pro M5 Max 36 GB run Qwen3.6 35B-A3B?

36 GB unified · 460 GB/s memory bandwidth · Qwen3.6 35B-A3B has 36B parameters (3B active per token).

Short answer

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

Best quantization for chat: 4-bit (19 GB of weights) on LM Studio. Generation: 95–160 tok/s, leaving about 8.3 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 WebUIExcellent4-bit95–160 tok/sunder 1 s8KDetails
Coding questionswith ContinueExcellent4-bit95–160 tok/s≈1 s16KDetails
Coding in a repositorywith AiderComfortable4-bit85–140 tok/s≈3 s32KDetails
Agentic codingwith OpenCodeAcceptable4-bit80–130 tok/s≈4 s32KDetails
Long documents (64K)with Open WebUIComfortable4-bit60–100 tok/s≈10 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 4-bit build with a 64K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_XL

Downloads 22.36 GB (UD-Q4_K_XL). 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.