NVIDIA and Eli Lilly Target AI Medicine Manufacturing

The pharmaceutical industry could be on the cusp of a manufacturing revolution as NVIDIA and Eli Lilly (Lilly) announce a US$1 bn investment in an AI-powered co-innovation lab. The partnership aims to address some of healthcare's most pressing challenges, from accelerating medicine discovery to strengthening pharmaceutical supply chains that serve patients worldwide.

The collaboration brings together Lilly's nearly 150 years of pharmaceutical expertise with NVIDIA's AI and computing capabilities. By combining these strengths, the companies hope to tackle critical healthcare challenges, including lengthy drug development timelines and manufacturing capacity constraints that can limit patient access to essential medicines.

The new lab will be based in the San Francisco Bay Area, where Lilly's domain experts in biology, science and medicine will work alongside NVIDIA's AI model builders and engineers. This proximity of expertise could enable the generation of large-scale data and the development of AI models designed to accelerate medicine development for patients.

Accelerating drug discovery for patients

The co-innovation lab infrastructure will be built on the NVIDIA BioNeMo platform and the NVIDIA Vera Rubin architecture. Over five years, the two companies will invest up to US$1 bn in talent, infrastructure and compute to support the initiative.

Jensen Huang, Founder and CEO of NVIDIA, says: "AI is transforming every industry and its most profound impact will be in life sciences. NVIDIA and Lilly are bringing together the best of our industries to invent a new blueprint for drug discovery – one where scientists can explore vast biological and chemical spaces 'in silico' (using computer simulation) before a single molecule is made."

The collaboration will focus on creating a continuous learning system that connects Lilly's agentic wet labs with computational dry labs, enabling round-the-clock AI-assisted experimentation to support biologists and chemists. This scientist-in-the-loop framework aims to enable experiments, data generation and AI model development to continuously inform and improve one another.

Using the NVIDIA BioNeMo Framework, an open source machine learning framework for building and training deep learning models for biopharma, the teams will work to build next-generation foundation and frontier models for biology and chemistry. The platform includes curated training recipes, data loaders and pretrained, optimised AI model architecture examples, all domain-specific and accelerated for enhanced performance.

Strengthening healthcare supply chains

Beyond drug discovery, the partnership could have significant implications for healthcare delivery and patient access. The companies plan to explore opportunities to apply AI across clinical development, manufacturing and commercial operations, integrating multimodal models, agentic AI, robotics and digital twins.

The use of physical AI and robotics in what the companies describe as an AI factory could help Lilly enhance its capacity to manufacture high-demand medications and strengthen supply chain reliability. This could prove particularly valuable for healthcare systems that have faced medication shortages in recent years.

"For nearly 150 years, we've been working to bring life-changing medicines to patients," says David Ricks, Chair and CEO of Lilly. "Combining our volume of data and scientific knowledge with NVIDIA's computational power and model-building expertise could reinvent drug discovery as we know it. By bringing together world-class talent in a startup environment, we're creating the conditions for breakthroughs that neither company could achieve alone."

The initiative expands on Lilly's previously announced AI supercomputer, which is the most powerful in the pharmaceutical industry. According to the companies, they will pioneer robotics and physical AI to accelerate and scale medicine discovery and production, potentially reducing the time between scientific breakthrough and patient access to new treatments.