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
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.
A high-engagement r/LocalLLaMA thread reports strong early results for Qwen3.5-35B-A3B in local agentic coding workflows. The original poster cites 100+ tokens/sec on a single RTX 3090 setup, while comments show mixed reproducibility and emphasize tooling, quantization, and prompt pipeline differences.
A high-signal r/LocalLLaMA benchmark post said moving Qwen 3.5 27B from mainline llama.cpp to ik_llama.cpp raised prompt evaluation from about 43 tok/sec to 1,122 tok/sec on a Blackwell RTX PRO 4000, with generation climbing from 7.5 tok/sec to 26 tok/sec.