# Eli Lilly Acquires DGX SuperPOD with 1,016 NVIDIA Blackwell Ultra GPUs for Drug Discovery

> Eli Lilly has switched on LillyPod, which it bills as the first Nvidia DGX SuperPOD built with DGX B300 systems, packing 1,016 Blackwell Ultra GPUs into a supercomputer dedicated entirely to drug discovery. The idea is to bring frontier-scale compute in-house for genomics, molecular modeling, and clinical development, rather than renting it piecemeal, and to run the kind of large biological and chemical models that were impractical at this scale before. It arrives alongside Lilly's broader AI push, including a multibillion-dollar research and licensing pact with Insilico Medicine, whose platform has already produced an AI-designed drug candidate and claims to have cut early timelines substantially. Together they sketch a pharma industry moving from AI as a bolt-on analysis tool toward AI-native discovery pipelines. The caution worth keeping is that compute and candidates are not the same as approved medicines, and biology has humbled plenty of well-funded bets. Still, a drugmaker standing up its own thousand-GPU cluster is a concrete sign of how seriously the industry now takes machine learning as core infrastructure, not experiment.

_Section: [Daily AI Updates](https://www.wortins.com/daily-ai) · Source: PharmExec · Published Thursday, September 3, 2026_

## Wortins' read

Eli Lilly has switched on LillyPod, which it bills as the first Nvidia DGX SuperPOD built with DGX B300 systems, packing 1,016 Blackwell Ultra GPUs into a supercomputer dedicated entirely to drug discovery. The idea is to bring frontier-scale compute in-house for genomics, molecular modeling, and clinical development, rather than renting it piecemeal, and to run the kind of large biological and chemical models that were impractical at this scale before. It arrives alongside Lilly's broader AI push, including a multibillion-dollar research and licensing pact with Insilico Medicine, whose platform has already produced an AI-designed drug candidate and claims to have cut early timelines substantially. Together they sketch a pharma industry moving from AI as a bolt-on analysis tool toward AI-native discovery pipelines. The caution worth keeping is that compute and candidates are not the same as approved medicines, and biology has humbled plenty of well-funded bets. Still, a drugmaker standing up its own thousand-GPU cluster is a concrete sign of how seriously the industry now takes machine learning as core infrastructure, not experiment.

## Source

[Read the full story at PharmExec](https://www.pharmexec.com/view/eli-lilly-insilico-enter-2-billion-research-licensing-agreement-advance-ai-drug-discovery)

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