Local AI Advisor

Can Desktop PC · Radeon AI PRO R9700 · 64 GB RAM run Mistral Small 3.2 (24B)?

32 GB VRAM + 64 GB RAM · 640 GB/s memory bandwidth · Mistral Small 3.2 (24B) has 24B parameters.

Short answer

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

Best quantization for chat: Q5_K_M (16 GB of weights) on Lemonade. Generation: 25–35 tok/s, leaving about 13 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 WebUIExcellentQ5_K_M25–35 tok/sunder 1 s8KDetails
Coding questionswith ContinueExcellentQ6_K25–30 tok/s≈3 s16KDetails
Coding in a repositorywith AiderAcceptableQ6_K20–30 tok/s≈10 s32KDetails
Agentic codingwith OpenCodeAcceptableQ6_K20–30 tok/s≈15 s32KDetails
Long documents (64K)with Open WebUIComfortableQ5_K_M18–25 tok/s≈55 s64KDetails

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 Q5_K_M build with a 64K 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=65536 before `ollama serve` for long prompts.

Different tool, context size or workload?

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