Local AI Advisor

Can MacBook Pro M2 Max 96 GB run Qwen3.5 122B-A10B?

96 GB unified · 400 GB/s memory bandwidth · Qwen3.5 122B-A10B has 125B parameters (10B active per token).

Short answer

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

Best quantization for chat: 4-bit (66 GB of weights) on LM Studio. Generation: 30–40 tok/s, leaving about 19 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-bit30–40 tok/s≈2 s8KDetails
Coding questionswith ContinueExcellent4-bit30–40 tok/s≈10 s16KDetails
Coding in a repositorywith AiderComfortable4-bit30–40 tok/s≈35 s32KDetails
Agentic codingwith OpenCodeAcceptable4-bit30–40 tok/s≈45 s32KDetails
Long documents (64K)with Open WebUIComfortable4-bit25–35 tok/s≈2 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 64K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3.5-122B-A10B-GGUF:Q4_K_M

This build is split into 3 files, which Ollama can't import from Hugging Face. Use llama.cpp or LM Studio instead.

Different tool, context size or workload?

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