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Lilly & NVIDIA Launch AI Factory to Accelerate Drug Discovery | NVIDIA Blog

by Rachel Kim – Technology Editor February 27, 2026
written by Rachel Kim – Technology Editor

INDIANAPOLIS – Eli Lilly and Company has activated LillyPod, the pharmaceutical industry’s most powerful supercomputer, built in collaboration with NVIDIA. The system, powered by 1,016 NVIDIA Blackwell Ultra GPUs, is now operational at Lilly’s Indianapolis campus, marking a significant investment in artificial intelligence for drug discovery and development.

The launch, announced Wednesday, culminates a partnership between Lilly and NVIDIA that began in October 2025 with the goal of creating an “AI factory” capable of managing the entire AI lifecycle, from data ingestion to model training and deployment. LillyPod delivers more than 9,000 petaflops of AI performance, assembled in just four months, according to Lilly officials.

“It’s a big day for us with the supercomputer coming on board, but it’s a day 150 years in the making,” said Diogo Rau, executive vice president and chief information and digital officer at Lilly. “LillyPod is a powerful symbol of who we are and why we do this operate: to make life better for people around the world. We are, right here, right now, at the right moment to advance biology in a way that has just never been done before.”

The supercomputer will be utilized across a range of scientific disciplines, including genomics, molecule design, single-cell biology, imaging, and manufacturing operations. Lilly’s genomics team will leverage LillyPod’s capabilities to analyze 700 terabytes of data using over 290 terabytes of high-bandwidth GPU memory. Thomas Fuchs, senior vice president and chief AI officer at Lilly, emphasized the necessity of such computational power, stating, “Computation is at the heart of biology and it is at the heart of science. Being able to compute at scale is not something optional for a company like ours, it is absolutely necessary. So we are building the computational future of medicine.”

LillyPod is designed to support the training of complex AI models, including protein diffusion models, small-molecule graph neural network models, and genomics foundation models. NVIDIA’s full-stack AI factory architecture, incorporating accelerated computing, NVIDIA Spectrum-X Ethernet networking, and optimized AI software, provides a secure and scalable platform for the highly regulated healthcare and life sciences sector. NVIDIA Mission Control software will manage the DGX SuperPOD, orchestrate workloads, monitor performance, and automate AI operations.

The infrastructure consists of nearly 5,000 connections built with over 1,000 pounds of fiber cables. Lilly has committed to powering its new AI infrastructure with 100% renewable electricity by 2030, utilizing efficient liquid cooling to minimize energy impact.

Lilly plans to make select models available through Lilly TuneLab, an AI and machine learning platform offering biotech companies access to drug discovery models built on proprietary Lilly data, generated at a cost exceeding $1 billion. TuneLab will also offer NVIDIA BioNeMo open foundation models for healthcare and life sciences, utilizing a federated learning infrastructure built on NVIDIA FLARE to ensure data privacy.

According to Lilly, the supercomputer addresses a key limitation in traditional drug discovery, which is constrained by the physical capacity of laboratory experiments. Yue Wang Webster, vice president of research and development informatics at Lilly, explained that the system allows scientists to simulate and evaluate billions of molecular hypotheses in a “dry lab” environment before committing to physical experiments, effectively breaking the “physical limit” of traditional research. Lilly employees can also use LillyPod to build chatbots, agentic workflows, and research lab agents.

“This machine is exactly how AI should be used,” said Fuchs. “It should be used for science. It should be used to lessen suffering and improve the human condition.”

Lilly will present further details about its collaboration with NVIDIA and a planned co-innovation AI lab at the upcoming NVIDIA GTC conference.

February 27, 2026 0 comments
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Technology

NVIDIA and Lilly CEOs Announce AI Co‑Innovation Lab for Drug Discovery

by Rachel Kim – Technology Editor January 17, 2026
written by Rachel Kim – Technology Editor

Here’s a breakdown of the key takeaways from the NVIDIA blog post, formatted for clarity:

NVIDIA at J.P. Morgan healthcare Conference – Key Highlights

Focus: Accelerating AI in Drug Revelation & Healthcare

Key Themes:

* AI’s Rapid Advancement: NVIDIA highlighted the exponential growth of AI capabilities (1 million times in the last 10 years) and its potential for the next decade.
* Collaboration with Lilly: Jensen Huang (NVIDIA CEO) discussed the potential of AI to address diseases of the aging brain with David Ricks (Lilly CEO).
* Recognition of AI Pioneers: NVIDIA gifted DGX Spark systems to over a dozen leaders in AI and drug discovery, acknowledging their contributions.

Award Recipients (Leaders & Their Companies):

* Zach Carpenter (VantAI): Neo model family for biomolecular design.
* Gabriele Corso (Boltz): Open-source biomolecular models.
* Evan Feinberg (Genesis Molecular AI): pearl model for protein/small molecule structure prediction.
* Chris Gibson & Najat Khan (Recursion): OpenPhenom vision transformer for microscopy data.
* Glen Gowers (Basecamp Research): EDEN genome language model.
* Brian Hie (Arc Institute): Collaboration on Evo 2 (Evo family of DNA language models).
* Max Jaderberg (Isomorphic): Extending AlphaFold’s capabilities.
* Simon kohl (Latent Labs): Latent-X generative models for protein sequence/structure.
* Joshua Meier (Chai Discovery): Chai family of generative AI models for molecular design.
* Tom Miller (Iambic Therapeutics): NeuralPLexer model for structure prediction.
* Alex Rives (Biohub): ESM family of protein language models.
* Alex Zhavoronkov (Insilico Medicine): Pharma.AI – integrated suite for drug discovery.

NVIDIA BioNeMo Platform Expansion:

* Clara Open Models: predicting RNA structures and drug synthesizability.
* BioNeMo Recipes: Tools to accelerate training, customization, and deployment of biological foundation models.
* BioNeMo Data Processing Libraries: Including nvMolKit (GPU-accelerated cheminformatics).

Other Collaborations Highlighted:

* Thermo Fisher: Building autonomous lab infrastructure with NVIDIA AI.
* Multiply Labs: Automating cell therapy manufacturing with robotic systems.

Resources:

* audio Recording of NVIDIA’s J.P. Morgan Healthcare address: https://jpmorgan.metameetings.net/events/healthcare26/sessions/317528-nvidia-corp/webcast/general_signin?gpu_only=true&kiosk=true
* NVIDIA Presentation Deck: https://s201.q4cdn.com/141608511/files/doc_presentations/2026/01/NVDA_JPM_Healthcare-2026.pdf

January 17, 2026 0 comments
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Technology

Multiply Labs Automates Cell Therapy Production with Robots

by Rachel Kim – Technology Editor January 16, 2026
written by Rachel Kim – Technology Editor

The Robotic Revolution in Cell Therapy Manufacturing

Multiply Labs is spearheading a dramatic shift in cell therapy production, mirroring the automation revolution that transformed the semiconductor industry. For decades,chip manufacturing relied on human technicians in sterile environments. Today, robots handle the intricate work with greater precision, speed, and cost-effectiveness. Multiply Labs is bringing that same paradigm shift to the world of cell therapies, addressing critical challenges in a rapidly growing field.

The company’s origin story highlights the stark contrast between traditional pharmaceutical manufacturing and the advanced techniques used in other industries. Co-founder Fred Parietti,while pursuing his PhD in robotics at MIT,connected with Alice Melocchi,who revealed the surprisingly manual and labor-intensive processes within cell therapy labs. “She showed me what she did in a lab and how arduous it was,and I couldn’t believe it — I thought drugs were made like chips,and this was insane but also real,” Parietti explains. This realization, coupled with a launch at YCombinator, fueled the creation of Multiply Labs in 2016.

Today, San Francisco-based Multiply Labs is working with leading companies like Kyverna Therapeutics and Legend Biotech, providing fully automated robotic systems designed to scale the production of gene-modified cell therapies.

The Promise of Cell Therapy and the Need for Automation

Cell therapies represent a groundbreaking approach to medicine,involving the manipulation of cells – either from a patient or a donor – to treat a wide range of diseases. These therapies hold immense promise for conditions like cancers, genetic disorders, autoimmune diseases, and neurological conditions. Though,the current manufacturing process is fraught with challenges.

Traditionally, cell therapy production has been a highly manual, artisanal process. Each treatment is often customized for a specific patient, making it incredibly expensive and susceptible to contamination or errors due to human handling. This is where Multiply Labs’ robotic systems come into play. By automating key steps in the manufacturing process, they ensure greater sterility, precision, and consistency.

from Clean Rooms to Robotic Clusters: A Paradigm Shift

The analogy to the semiconductor industry is apt. just as chip manufacturing transitioned from technicians in “bunny suits” working in clean rooms to fully automated facilities, multiply Labs is ushering in a new era for bioscience. This shift promises not only increased precision and reduced contamination but also the ability to leverage physical AI to optimize manufacturing processes.

Multiply Labs’ systems utilize NVIDIA Omniverse to create highly accurate digital twins of laboratory environments. These virtual replicas allow for the simulation and optimization of processes without the risk of damaging valuable cell cultures. Furthermore, the NVIDIA Isaac Sim robotics simulation framework is used to train robots on the complex skills required for cell therapy development.

Looking ahead, Multiply labs is even developing humanoid robots powered by the NVIDIA Isaac GR00T humanoid foundation robot model.These robots are designed to assist lab personnel with tasks requiring dexterity and precision, further enhancing hygiene and efficiency.

The Power of Imitation Learning in Robotics

A crucial element of Multiply Labs’ approach is the use of imitation learning.This technique allows robots to learn complex tasks by observing and replicating the movements of human experts.By analyzing video demonstrations of skilled scientists performing cell therapy procedures, the robots can acquire the necessary skills without requiring extensive manual programming.

This is particularly valuable in cell therapy, where much of the expertise is tacit – meaning it’s based on years of experience and intuition rather than documented procedures. Imitation learning effectively captures this knowledge and translates it into robotic control policies, ensuring that the robots perform tasks with the same level of skill and precision as the human experts.

How Imitation Learning Works in Practice

  • Data Collection: Experts perform the desired cell therapy procedures while being recorded.
  • Data Processing: The video data is analyzed to extract key movements and actions.
  • Robot Training: The robot is trained using the extracted data, learning to replicate the expert’s movements.
  • Validation & Refinement: The robot’s performance is validated and refined through further training and testing.

Looking Ahead: The Future of Cell Therapy Manufacturing

Multiply Labs is not simply automating existing processes; it’s fundamentally rethinking how cell therapies are manufactured. by embracing robotics, simulation, and AI, the company is paving the way for a future where these life-saving treatments are more accessible, affordable, and reliable.

The implications of this technology extend beyond cost reduction and increased efficiency. Automated manufacturing can also accelerate the development of new cell therapies, enabling researchers to test and refine treatments more quickly. As the field of cell therapy continues to evolve, Multiply Labs is poised to play a pivotal role in shaping its future.

Key Takeaways

  • multiply labs is automating cell therapy manufacturing using robotics and AI.
  • This automation addresses critical challenges in the field, including high costs, contamination risks, and scalability issues.
  • The company leverages NVIDIA Omniverse and Isaac Sim for digital twin creation and robot training.
  • Imitation learning is a key technology enabling robots to replicate the skills of human experts.
  • The future of cell therapy manufacturing will be defined by automation, precision, and scalability.
January 16, 2026 0 comments
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