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
HN focused less on whether local LLMs fully replace frontier models and more on where they already make sense. The thread turned into a practical debate about Gemma, Qwen, agentic coding, memory limits, cost, and privacy.
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 users reacted strongly to a small but practical vLLM nightly change. The new Qwen3+ streaming parser is aimed at mid-turn stops and streaming tool-call failures that can break Qwen3.6 agent loops.