Achieving 95% SimpleQA Accuracy on a Single RTX 3090 with Local Deep Research

Why you need a local deep research engine?

When writing a literature review on a rare disease, researchers typically open many browser tabs, copy‑paste results from PubMed, arXiv, and personal PDFs, and spend hours organizing the information. Local Deep Research lets you type a question, automatically searches academic databases and your own documents, runs multi‑turn reasoning with an LLM, and returns a structured report with citations—all executed locally so no data leaves your machine.

Technical architecture: more than a wrapper

The project follows a developer‑friendly, plugin‑style design. The LLM layer can use llama.cpp , Ollama , OpenAI‑compatible APIs, or Google Gemini, while the search layer integrates over ten engines such as arXiv, PubMed, SearXNG, and Google Custom Search. For data persistence it employs an SQLCipher encrypted database , ensuring that research notes, conversation history, and knowledge‑base entries are stored securely on‑device.

Performance is highlighted by a SimpleQA benchmark result: using the Qwen3.6‑27B model on a single RTX 3090, the system achieves roughly 95% accuracy , comparable to many closed‑source API services, with the only cost being electricity and hardware depreciation.

Three‑minute setup: from zero to first deep research

If you have Docker installed, three commands are enough: start Ollama as the inference backend, launch SearXNG as the search engine, and run the Local Deep Research container. No manual model path configuration or API keys are required.

Developers who prefer native installation can use the PyPI package or follow the detailed Linux/macOS guides. The documentation also provides a complete Docker Compose file that brings up all services with a single command, dramatically lowering the entry barrier.

Who should try it now?

The project targets three clear user groups: academic researchers who need to extract information from massive paper collections without risking data leakage; privacy‑conscious developers who want AI assistance without cloud bindings; and local AI enthusiasts who own consumer GPUs such as RTX 3090/4090 and wish to experiment with cutting‑edge research capabilities.

For enterprise scenarios, the encrypted storage and on‑premise deployment make it suitable for regulated industries—finance, healthcare, legal—where compliance and auditability are mandatory.

In an era where AI tools are increasingly centralized, Local Deep Research offers a counter‑trend: powerful capabilities that stay on your hardware, giving you true data sovereignty.

With nearly 5,000 GitHub stars and hundreds of new stars each day, the community has already signaled strong approval. If you are tired of API quotas, privacy policies, and network latency, you can pull the Docker image tonight and start your first local deep‑research session.