Local AI Advisor

Can Desktop PC · GeForce RTX 4090 · 32 GB RAM run Qwen2.5-Coder 32B Instruct?

24 GB VRAM + 32 GB RAM · 1008 GB/s memory bandwidth · Qwen2.5-Coder 32B Instruct has 32.8B parameters.

Short answer

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

Best quantization for chat: Q4_K_M (19 GB of weights) on Ollama. Generation: 30–40 tok/s, leaving about 1.0 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 WebUIExcellentQ4_K_M30–40 tok/sunder 1 s8KDetails
Coding questionswith ContinueAcceptableQ3_K_M35–45 tok/s≈3 s16KDetails
Coding in a repositorywith AiderBorderlineQ4_K_M7–9 tok/s≈10 s32KDetails
Agentic codingwith OpenCodeBorderlineQ4_K_M6–9 tok/s≈15 s32KDetails
Long documents (64K)with Open WebUIBorderlineQ4_K_M6–8 tok/s≈30 s32KDetails

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 16K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen2.5-Coder-32B-Instruct-GGUF:Q3_K_M

Downloads 15.94 GB (Q3_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.