Local AI Advisor

Can Desktop PC · Radeon RX 7900 XT · 32 GB RAM run Qwen3-Coder 30B-A3B Instruct?

20 GB VRAM + 32 GB RAM · 800 GB/s memory bandwidth · Qwen3-Coder 30B-A3B Instruct has 30.5B parameters (3.3B active per token).

Short answer

Yes, excellent for chat; borderline for 64K-token documents.

Best quantization for chat: Q3_K_M (14 GB of weights) on Lemonade. Generation: 170–280 tok/s, leaving about 3.5 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_M170–280 tok/sunder 1 s8KDetails
Coding questionswith ContinueComfortableQ3_K_M140–240 tok/s≈2 s16KDetails
Coding in a repositorywith AiderAcceptableQ3_K_M95–160 tok/s≈7 s32KDetails
Agentic codingwith OpenCodeAcceptableQ3_K_M85–140 tok/s≈10 s32KDetails
Long documents (64K)with Open WebUIBorderlineQ4_K_M12–20 tok/s≈60 s64KDetails

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 Q3_K_M build with a 32K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q3_K_M

Downloads 14.71 GB (Q3_K_M). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=32768 before `ollama serve` for long prompts.

Different tool, context size or workload?

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