Local AI Advisor

Can Mac mini M6 32 GB run Devstral Small 2 (24B)?

32 GB unified · 170 GB/s memory bandwidth · Devstral Small 2 (24B) has 24B parameters.

Short answer

Yes, acceptable for chat; borderline for coding in a repository.

Best quantization for chat: 4-bit (13 GB of weights) on LM Studio. Generation: 6–10 tok/s, leaving about 9.3 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 WebUIAcceptable4-bit6–10 tok/s≈3 s8KDetails
Coding questionswith ContinueAcceptable4-bit6–10 tok/s≈20 s16KDetails
Coding in a repositorywith AiderBorderline4-bit5–9 tok/s≈60 s32KDetails
Agentic codingwith OpenCodeBorderline5-bit4.2–7 tok/s≈85 s32KDetails
Long documents (64K)with Open WebUIBorderline4-bit3.7–6 tok/s≈5 min64KDetails

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 4-bit build with a 16K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF:Q4_K_M

Downloads 14.33 GB (Q4_K_M). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=16384 before `ollama serve` for long prompts.

Different tool, context size or workload?

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