Local AI Advisor

Can MacBook Pro M3 Max 96 GB run Mistral Small 3.2 (24B)?

96 GB unified · 300 GB/s memory bandwidth · Mistral Small 3.2 (24B) has 24B parameters.

Short answer

Yes, comfortable for chat; borderline for agentic coding.

Best quantization for chat: 5-bit (16 GB of weights) on LM Studio. Generation: 11–15 tok/s, leaving about 68 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 WebUIComfortable5-bit11–15 tok/s≈5 s8KDetails
Coding questionswith ContinueAcceptable5-bit11–15 tok/s≈30 s16KDetails
Coding in a repositorywith AiderAcceptable5-bit10–13 tok/s≈2 min32KDetails
Agentic codingwith OpenCodeBorderline5-bit9–13 tok/s≈3 min32KDetails
Long documents (64K)with Open WebUIBorderline6-bit7–9 tok/s≈9 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 5-bit build with a 32K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF:Q5_K_M

Downloads 16.76 GB (Q5_K_M). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=32768 before `ollama serve` for long prompts.

Different tool, context size or workload?

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