CPHI Milan newsletter
PCI High Potent Manufacturing
LB Bohle – 10.06.2025
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DDL 2026

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Nipro August 26

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Merxin March 2026

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Lifecore Biomedical

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Solstice

AbbVie and Cerebras Systems partner to accelerate AI research

Link up between AbbVie and Cerebras Systems boosts AI platforms, while radically reducing energy consumption.

AbbVie and Cerebras Systems have reached a landmark in progressing AbbVie’s artificial intelligence (AI) work.

The company has utilised Cerebras CS-2 from Cerebras systems, which accelerates AI, while radically reducing power consumption.

In using a Cerebras CS-2 on biomedical natural language processing models, AbbVie achieved performance over 128 times that of a typical graphics processing unit (GPU), while using a third of the energy.

“A common challenge we experience with programming and training is providing sufficient GPU cluster resources for sufficient periods of time,” said Brian Martin, head of (AI) at AbbVie.

“The CS-2 system will provide wall-clock improvements that alleviate much of this challenge while providing a simpler programming model that accelerates our delivery by enabling our teams to iterate more quickly and test more ideas,” he added.

AbbVie, a global biopharmaceutical company, has a focus on cutting-edge R&D across immunology, neuroscience, oncology and virology. In pursuing innovative treatments, AbbVie utilises large and sophisticated AI language models to build its translation service, Abbelfish.

The service accurately translates and makes vast libraries of biomedical literature searchable, across 180 languages using large, state-of-the-art transformer models.

“At Cerebras Systems, our goal is to enable AI that accelerates our customer’s mission,” explained Andrew Feldman, CEO and co-founder of Cerebras Systems.

“It’s not enough to provide customers with the fastest AI in the market – it also must be the most energy-efficient and the easiest to deploy. It’s incredible to see AbbVie not only accelerating their massive language models but doing so while consuming a fraction of the energy used by legacy solutions.”

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