95.7% SimpleQA on a Single RTX 3090: Qwen3.6-27B with Agentic Search
Original: We are finally there: Qwen3.6-27B + agentic search; 95.7% SimpleQA on a single 3090, fully local View original →
The Achievement
An r/LocalLLaMA post (297 points) from the LDR maintainer reports 95.7% on OpenAI SimpleQA benchmark, fully locally on a single RTX 3090 with 24GB VRAM.
Setup
- Hardware: RTX 3090, 24GB
- Model: Qwen3.6:27b via Ollama
- Strategy: LangGraph agent with tool-calling and parallel subtopic decomposition
Why It Matters
SimpleQA is where frontier cloud models score 90 to 98%. Reaching 95.7% locally on consumer hardware is a meaningful milestone. Combining local LLM reasoning with agentic web search dramatically outperforms single-pass inference.
Related Articles
A local LLM researcher achieved 95.7% on SimpleQA using Qwen3.6-27B with agentic search on a single consumer GPU.
LocalLLaMA cared less about headline speed than a Qwen3.6 setup on one RTX 3090 that reached 218K context and stopped crashing on long tool outputs.
LocalLLaMA lit up at the idea that a 27B model could tie Sonnet 4.6 on an agentic index, but the thread turned just as fast to benchmark gaming, real context windows, and what people can actually run at home.
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