Local AI Advisor

Can Laptop · Ryzen AI 9 HX 370 (Radeon 890M) · 32 GB run Qwen3 30B-A3B Instruct 2507?

32 GB unified · 120 GB/s memory bandwidth · Qwen3 30B-A3B Instruct 2507 has 30.5B parameters (3.3B active per token).

Short answer

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

Best quantization for chat: Q4_K_M (17 GB of weights) on Lemonade. Generation: 20–35 tok/s, leaving about 4.7 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 WebUIComfortableQ4_K_M20–35 tok/s≈2 s8KDetails
Coding questionswith ContinueAcceptableQ3_K_M20–35 tok/s≈15 s16KDetails
Coding in a repositorywith AiderBorderlineQ4_K_M12–20 tok/s≈50 s32KDetails
Agentic codingwith OpenCodeBorderlineQ4_K_M11–18 tok/s≈70 s32KDetails
Long documents (64K)with Open WebUIBorderlineQ3_K_M7–11 tok/s≈5 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 Q3_K_M build with a 16K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3-30B-A3B-Instruct-2507-GGUF:Q3_K_M

Downloads 14.71 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.