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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 Unveils Earth‑2: World’s First Fully Open AI Weather Models and Tools

by Rachel Kim – Technology Editor February 8, 2026
written by Rachel Kim – Technology Editor

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The AI Revolution in Weather Forecasting: A Deep Dive

The AI Revolution in Weather Forecasting: A Deep Dive

For ⁣centuries, humans ⁣have looked ⁤to the skies, attempting to predict​ the weather. From observing cloud formations to complex atmospheric measurements, the pursuit ‌of ‍accurate forecasting has been a constant.Today, we‌ stand on⁢ the cusp ⁤of ‌a new ⁣era in weather prediction, driven by ⁤the power⁤ of Artificial Intelligence (AI). Accurate weather forecasting isn’t just about knowing ​whether to pack‍ an ⁤umbrella; it’s‍ a critical component of⁣ safeguarding lives,protecting our environment,and making informed decisions across vital industries like agriculture,energy,and public health. This article explores the transformative impact of AI⁢ on weather forecasting, detailing the advancements, challenges, and future‌ possibilities.

The⁣ Limitations of Customary Weather Forecasting

Traditional ⁢weather forecasting relies on⁢ numerical weather prediction (NWP) models. These models use complex mathematical equations to simulate the behaviour of the​ atmosphere.While incredibly sophisticated, ‍NWP​ models have inherent⁤ limitations:

  • Computational Cost: Running these models requires immense computing power,‍ limiting the resolution and speed of ⁤forecasts.
  • Data Assimilation: ‍integrating observational data⁣ (from satellites, weather stations, buoys, ‌etc.) into the models⁣ is a complex⁤ process, and errors in data assimilation⁣ can propagate through the forecast.
  • Chaos Theory: The atmosphere is a ‍chaotic system, meaning small initial differences can lead to drastically​ different outcomes. This limits⁢ the ⁤predictability of​ forecasts, especially ‌for longer time horizons.
  • Parameterization: ​ Many atmospheric processes occur at scales too small to be explicitly resolved by ⁤NWP models. These processes are represented using simplified approximations called ⁤parameterizations, which introduce uncertainty.

These⁤ limitations mean that even the best traditional forecasts are imperfect, particularly when ​predicting localized or⁣ rapidly changing weather ‌events.

How AI is⁤ Transforming Weather Prediction

AI, particularly​ machine learning (ML), ‌offers a powerful set‍ of tools ‍to overcome the limitations of traditional forecasting methods. here’s how:

AI-Powered Models: A New Approach

AI models learn patterns from vast amounts of past weather data. Unlike NWP models that solve equations, AI​ models identify correlations and relationships within ‍the data to make ​predictions. Several approaches are ‌being used:

  • Deep Learning: ‍Deep‌ neural networks, with their multiple layers, can capture complex‍ non-linear relationships in weather data. ​ Models like GraphCast (Google DeepMind) and Pangu-Weather (Huawei) have demonstrated remarkable skill in medium-range forecasting, often outperforming⁤ traditional NWP systems.
  • Convolutional ‍Neural Networks (CNNs): CNNs are particularly effective at processing spatial data, like weather maps, and identifying⁣ patterns in⁣ images.
  • recurrent ⁣Neural Networks (RNNs): RNNs ⁤are designed to⁤ handle sequential data, making them suitable for time-series forecasting like predicting ‍temperature changes over time.
  • Generative Adversarial‍ Networks ‍(GANs): GANs⁢ can generate ​realistic weather scenarios, helping to improve the ⁤accuracy and‍ reliability of ensemble forecasts.

Speed and Efficiency

AI models‍ can generate‌ forecasts much faster than traditional NWP models. GraphCast,⁢ for example, can produce a 10-day global forecast in​ under a minute, compared to hours‌ for‌ some‌ NWP systems. This speed is crucial for timely warnings of severe weather events.

Improved Accuracy

Recent studies have shown that AI models ‌can achieve comparable or even superior accuracy​ to traditional ⁢models, especially‌ for medium-range forecasts (3-10 days). Such as,GraphCast has shown ​notable improvements in predicting extreme weather events like tropical ​cyclones and atmospheric rivers. Pangu-Weather has demonstrated similar capabilities, achieving state-of-the-art performance in global weather⁤ forecasting.

Nowcasting Enhancement

“Nowcasting” – predicting weather conditions in the very near ⁢future (0-6 hours)‍ – is critical ‍for applications like aviation and emergency management. AI excels at nowcasting by analyzing real-time data from radar, satellites, and surface observations to identify and track rapidly developing weather systems. ⁢ AI-powered ‌nowcasting systems can provide highly localized and‌ accurate predictions of rainfall,thunderstorms,and other hazardous weather.

Real-World Applications and Impact

The advancements in AI-powered weather forecasting are‍ already ​having a significant impact across various sectors:

  • Agriculture: Farmers can use AI-driven forecasts to​ optimize‌ planting, irrigation, and harvesting⁣ schedules, reducing crop ⁤losses ⁣and improving yields.
  • Energy: Energy companies can ⁤better‍ predict demand and optimize the ‍generation and distribution of electricity, especially ​from renewable sources like solar and‌ wind.
February 8, 2026 0 comments
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