Local AI Advisor

Can Desktop PC · GeForce RTX 4080 SUPER · 32 GB RAM run Mistral Small 3.2 (24B)?

16 GB VRAM + 32 GB RAM · 736 GB/s memory bandwidth · Mistral Small 3.2 (24B) has 24B parameters.

Short answer

Yes, excellent for chat; borderline for coding questions.

Best quantization for chat: Q3_K_M (11 GB of weights) on Ollama. Generation: 35–50 tok/s, leaving about 1.6 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 WebUIExcellentQ3_K_M35–50 tok/sunder 1 s8KDetails
Coding questionswith ContinueBorderlineQ4_K_M13–17 tok/s≈3 s16KDetails
Coding in a repositorywith AiderBorderlineQ4_K_M7–10 tok/s≈10 s32KDetails
Agentic codingwith OpenCodeBorderlineQ4_K_M7–10 tok/s≈15 s32KDetails
Long documents (64K)with Open WebUIBorderlineQ3_K_M4.7–6 tok/s≈65 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 Q3_K_M build with a 8K 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:Q3_K_M

Downloads 11.47 GB (Q3_K_M). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=8192 before `ollama serve` for long prompts.

Different tool, context size or workload?

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