Lovelace Shows Locally Hosted AI Can Match Cloud Research Systems

Lovelace announced in a press release new benchmark results demonstrating that enterprises can produce AI-powered research comparable to Google's Gemini Deep Research using open-source models running entirely on local hardware.

Lovelace's benchmark used its YottaGraph context engine together with a locally hosted Gemma 4 model to carry out 18 advanced investment banking research scenarios. The local system delivered research quality statistically equal to Gemini Deep Research while lowering inference costs from about seven dollars per report to roughly one cent in electricity.

The benchmark replaces Lovelace's last remaining cloud component, enabling an AI research workflow that operates fully within an organization's infrastructure. This design allows companies to keep sensitive data internal, reduce recurring cloud expenses, and simplify compliance.

According to Lovelace, the results indicate that organizations can achieve high-quality research outcomes through open-source AI and superior contextual data management, rather than relying on the largest cloud models. The full methodology and technical details are available on the company's website.