Local AI Advisor

Can Mac Studio M2 Max 64 GB run Mistral Small 4 (119B-A6.5B)?

64 GB unified · 400 GB/s memory bandwidth · Mistral Small 4 (119B-A6.5B) has 119.4B parameters (6.5B active per token).

Short answer

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

Best quantization for chat: 3-bit (49 GB of weights) on LM Studio. Generation: 60–85 tok/s, leaving about 3.8 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-bit60–85 tok/s≈2 s8KDetails
Coding questionswith ContinueBorderline3-bit60–80 tok/s≈9 s16KDetails
Coding in a repositorywith AiderBorderline3-bit55–75 tok/s≈30 s32KDetails
Agentic codingwith OpenCodeBorderline3-bit55–70 tok/s≈40 s32KDetails
Long documents (64K)with Open WebUIBorderline3-bit45–60 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/Mistral-Small-4-119B-2603-GGUF:UD-Q3_K_XL

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.