Local AI Advisor

Can MacBook Pro M5 Max 128 GB run Qwen3.6 35B-A3B?

128 GB unified · 614 GB/s memory bandwidth · Qwen3.6 35B-A3B has 36B parameters (3B active per token).

Short answer

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

Best quantization for chat: 6-bit (28 GB of weights) on LM Studio. Generation: 90–150 tok/s, leaving about 89 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 WebUIExcellent6-bit90–150 tok/sunder 1 s8KDetails
Coding questionswith ContinueExcellent6-bit90–150 tok/s≈1 s16KDetails
Coding in a repositorywith AiderExcellent6-bit80–140 tok/s≈3 s32KDetails
Agentic codingwith OpenCodeExcellent6-bit80–130 tok/s≈3 s32KDetails
Long documents (64K)with Open WebUIExcellent6-bit65–110 tok/s≈9 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 6-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-Q6_K_XL

Downloads 31.84 GB (UD-Q6_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.