Local AI Advisor

Can Desktop PC · Ryzen 9 9950X (no GPU) · 64 GB DDR5 run Qwen3 30B-A3B Instruct 2507?

64 GB RAM, no GPU · Qwen3 30B-A3B Instruct 2507 has 30.5B parameters (3.3B active per token).

Short answer

Yes, comfortable for chat; borderline for agentic coding.

Best quantization for chat: Q5_K_M (20 GB of weights) on llama.cpp (llama-server). Generation: 11–18 tok/s, leaving about 31 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 WebUIComfortableQ5_K_M11–18 tok/s≈3 s8KDetails
Coding questionswith ContinueAcceptableQ5_K_M9–16 tok/s≈20 s16KDetails
Coding in a repositorywith AiderAcceptableQ5_K_M7–12 tok/s≈65 s32KDetails
Agentic codingwith OpenCodeBorderlineQ6_K6–9 tok/s≈2 min32KDetails
Long documents (64K)with Open WebUIAcceptableQ6_K3.5–6 tok/s≈6 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 Q6_K build with a 64K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3-30B-A3B-Instruct-2507-GGUF:Q6_K

Downloads 25.09 GB (Q6_K). 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.