Local AI Advisor

Can MacBook Air M2 24 GB run Qwen3.6 35B-A3B?

24 GB unified · 100 GB/s memory bandwidth · Qwen3.6 35B-A3B has 36B parameters (3B active per token).

Short answer

It loads, but it's borderline even for chat.

Best quantization for chat: 3-bit (15 GB of weights) on LM Studio. Generation: 35–50 tok/s, leaving about 0.7 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 WebUIBorderline3-bit35–50 tok/s≈2 s8KDetails
Coding questionswith ContinueDoes Not Fit————Details
Coding in a repositorywith AiderDoes Not Fit————Details
Agentic codingwith OpenCodeDoes Not Fit————Details
Long documents (64K)with Open WebUIBorderline3-bit20–25 tok/s≈3 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 3-bit build with a 8K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q3_K_XL

Downloads 16.85 GB (UD-Q3_K_XL). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=8192 before `ollama serve` for long prompts.

Different tool, context size or workload?

These pages use typical settings. The full check models your exact setup.