Local AI Advisor

Can Desktop PC · GeForce RTX 4060 Ti 16GB · 32 GB RAM run Qwen3-Coder 30B-A3B Instruct?

16 GB VRAM + 32 GB RAM · 288 GB/s memory bandwidth · Qwen3-Coder 30B-A3B Instruct has 30.5B parameters (3.3B active per token).

Short answer

It loads, but it's borderline even for chat.

Best quantization for chat: Q4_K_M (17 GB of weights) on Ollama. Generation: 30–45 tok/s, leaving about 0.0 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 WebUIBorderlineQ4_K_M30–45 tok/sunder 1 s8KDetails
Coding questionswith ContinueBorderlineQ4_K_M25–40 tok/s≈4 s16KDetails
Coding in a repositorywith AiderBorderlineQ5_K_M12–19 tok/s≈20 s32KDetails
Agentic codingwith OpenCodeBorderlineQ5_K_M10–17 tok/s≈25 s32KDetails
Long documents (64K)with Open WebUIBorderlineQ6_K4.9–8 tok/s≈2 min64KDetails

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 Q4_K_M build with a 8K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_M

Downloads 18.56 GB (Q4_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.