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SLB Industrializes AI for the Energy Industry with NVIDIA

Companies will develop modular data center infrastructure and generative AI models for large-scale deployments.
Date: 2026-06-19


SLB’s technology center in Louisiana: a 3.1 million square-foot facility dedicated to manufacturing for its modular data center business.

HOUSTON -- Global energy technology company SLB (NYSE: SLB) announced an expansion of its technology collaboration with NVIDIA to design and deploy critical AI infrastructure and models for the energy industry.

The work focuses on three strategic elements:

·Modular design for data centers: SLB will serve as the modular design partner for NVIDIA DSX AI factories. This modular approach, where components are manufactured offsite, will drive increased quality and reliability while also reducing costs, labor constraints and lead times. It also enables rapid and flexible scaling, which allows customers to expand data center capacity quickly as demand grows.
·AI Factory for Energy: SLB will work with NVIDIA to develop an “AI Factory for Energy,” a reference environment powered by domain-specific generative AI models and industrial-scale agentic AI. This will run on SLB’s digital platforms to help energy companies scale AI for their data and operations.
·Accelerated computing for SLB digital platforms: The companies will optimize the processing of large datasets and AI models across SLB digital platforms using the latest NVIDIA AI infrastructure, aiming to establish new benchmarks for performance and efficiency in energy applications.

“The winners in AI will be companies with the best data, the deepest domain expertise and the ability to scale,” said Demos Pafitis, SLB’s chief technology officer. “By collaborating with NVIDIA to advance modular data‑center construction and harness our domain expertise and digital platforms, we’re enabling the energy industry to deploy AI at scale and transform operational data into smarter decisions.”

“AI is becoming the engine of a new industrial revolution, and the energy industry is at its forefront,” said Vladimir Troy, vice president of AI Infrastructure at NVIDIA. “Building AI Factory infrastructure and domain models is needed to turn massive amounts of energy data into actionable insights and accelerate more efficient and sustainable energy systems.”

Energy companies generate vast amounts of operational data across subsurface, production and energy infrastructure, which makes decision-making somewhat slow and siloed. By combining NVIDIA Omniverse libraries and NVIDIA Nemotron open models with SLB digital and AI platforms, the collaboration aims to accelerate the transformation of that data into actionable insights. The work spans traditional machine learning, generative AI and emerging agentic AI technologies designed to improve performance and support reliable, efficient and lower-carbon energy systems.

Today’s announcement builds on a relationship that began in 2008, when NVIDIA accelerated computing was first used to enhance SLB subsurface visualization and seismic imaging software. In 2024, the companies announced plans to develop generative artificial intelligence solutions for the energy sector using NVIDIA software integrated with SLB’s Delfi™ digital platform and Lumi™ data and AI platform.

Key points

·SLB and NVIDIA are expanding their long-standing technology collaboration to design and deploy critical AI infrastructure and models for the energy industry.
·SLB will serve as the design partner for modular DSX AI data centers, using modular and scalable off-site construction and deployment to shorten lead times.
·SLB and NVIDIA will develop an “AI Factory for Energy,” a reference environment powered by domain-specific generative AI models and industrial-scale agentic AI running within SLB’s digital platforms to help energy companies scale AI for their data and operations.
·The collaboration will optimize the processing of large datasets and AI models across SLB digital platforms using the latest NVIDIA technologies.
·The expanded work builds on a relationship that began in 2008 and reflects the industry’s shift from AI experimentation to enterprise-scale deployment.



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