Nvidia and U.S. drugmaker Eli Lilly said they will spend $1 billion over five years to build a joint AI research lab in the San Francisco Bay Area.

The companies said the new facility will use Nvidia’s newest Vera Rubin generation artificial intelligence chips. Researchers from both firms will work together at the site.

The announcement came Monday at the start of the JPMorgan Healthcare Conference in San Francisco. The firms said they will announce the exact location of the lab in March.

The plan follows a recent move by Lilly to build a large in-house supercomputer. That system will use more than 1,000 of Nvidia’s current Grace Blackwell AI chips.

A supercomputer is a group of powerful computers linked together. It can process huge amounts of data at very high speed, which helps train advanced AI systems.

Drugmakers increasingly use AI to speed up research. These systems can analyze data, predict how molecules behave, and suggest new drug candidates faster than traditional methods.

The goal is to shorten the time needed to discover and design new treatments. Companies hope this can reduce costs and bring medicines to patients sooner.

Nvidia and Lilly did not say whether Nvidia will provide cash directly to Lilly. They also did not say if any funding would go toward purchasing Nvidia chips.

Some past Nvidia investments have drawn questions about whether money cycles back to buy its own products. The companies did not address that issue.

Nvidia has focused on supplying AI software and open-source models to biotech firms. Drugmakers can adapt those tools to build their own research platforms using Nvidia hardware.

On Monday, Nvidia released several new AI models for biotechnology. One updated model helps ensure that drugs designed by AI can actually be made in physical labs.

This step aims to close the gap between computer-generated designs and real-world chemistry. It helps researchers avoid ideas that look good on screen but fail in practice.

Nvidia healthcare vice president Kimberly Powell said both companies are committing new resources to the project. She said teams will work side by side to generate fresh data to train future biotech AI models.