Local AI Advisor

Can Desktop PC · GeForce RTX 5090 · 32 GB RAM run Qwen3-Coder 30B-A3B Instruct?

32 GB VRAM + 32 GB RAM · 1792 GB/s memory bandwidth · Qwen3-Coder 30B-A3B Instruct has 30.5B parameters (3.3B active per token).

Short answer

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

Best quantization for chat: Q6_K (24 GB of weights) on Ollama. Generation: 240–320 tok/s, leaving about 5.9 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 WebUIExcellentQ6_K240–320 tok/sunder 1 s8KDetails
Coding questionswith ContinueExcellentQ6_K220–290 tok/sunder 1 s16KDetails
Coding in a repositorywith AiderComfortableQ6_K160–220 tok/s≈2 s32KDetails
Agentic codingwith OpenCodeComfortableQ6_K150–200 tok/s≈2 s32KDetails
Long documents (64K)with Open WebUIExcellentQ5_K_M95–130 tok/s≈7 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/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q5_K_M

Downloads 21.73 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.