Can Desktop PC · GeForce RTX 3060 12GB · 32 GB RAM run Qwen3 8B?

12 GB VRAM + 32 GB RAM · 360 GB/s memory bandwidth · Qwen3 8B has 8.2B parameters.

Short answer

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

Best quantization for chat: Q6_K (6.5 GB of weights) on Ollama. Generation: 25–40 tok/s, leaving about 2.6 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.

Workload	Verdict	Quant	Generation	First reply	Context
Chatwith Open WebUI	Excellent	Q6_K	25–40 tok/s	under 1 s	8K	Details
Coding questionswith Continue	Comfortable	Q6_K	25–40 tok/s	≈4 s	16K	Details
Coding in a repositorywith Aider	Borderline	Q5_K_M	20–35 tok/s	≈15 s	32K	Details
Agentic codingwith OpenCode	Borderline	Q5_K_M	19–30 tok/s	≈20 s	32K	Details
Long documents (64K)with Open WebUI	Acceptable	Q3_K_M	20–35 tok/s	≈40 s	32K	Details

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/Qwen3-8B-GGUF:Q3_K_M

Downloads 4.12 GB (Q3_K_M). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=32768 before `ollama serve` for long prompts.

Other models for Desktop PC · GeForce RTX 3060 12GB · 32 GB RAM

The best-rated models for chat on this machine.

Everything Desktop PC · GeForce RTX 3060 12GB · 32 GB RAM can run

Cheapest machines for Qwen3 8B

Hardware rated comfortable or better for chat, lowest price first.

Different tool, context size or workload?

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

Open sourceCheck my exact setup