Energy, oil & gas

Harness the power of wafer-scale AI computing with Cerebras to accelerate energy research and development, enable more reliable and efficient production and delivery.

Industry Challenge:

AI and HPC are powering revolutionary discovery in energy and oil & gas for a range of applications — from reservoir modeling to production, distribution and delivery. However, these are data-hungry applications that typically require a supercomputer to run.

With Cerebras, researchers and data scientists can unlock supercomputer-scale AI and HPC compute performance in a single CS-2 system or a cluster of CS-2s that is more compact, power efficient, and easier to program than a traditional cluster. Accelerate discovery and innovation by orders of magnitude.

Use Case

Exploration geophysics

Geoscientists employ massive multi-modal datasets and computationally-intensive signal processing algorithms to map out subsurface environments.

With its massive 850,000 core wafer-scale sparse linear algebra processor, the CS-2 can process vastly larger datasets in a fraction of the time of traditional systems, enabling broader search and greater precision far faster than traditional CPU and GPU clusters.

Use Case

Reservoir modeling

AI and HPC methods are increasingly used together to build better models of subsurface physics and fluid flow. The unique combination of massive sparse compute, high bandwidth memory and communication of our wafer-scale engine make it the ideal processor for both types of work.

The CS-2 accelerates AI and HPC to improve reservoir modeling accuracy and enable more efficient operations with a reduced environmental footprint.

Use Case

Sensor data processing and predictive maintenance

State of the art energy facilities such as refineries, pipelines, power plants and distribution grids are packed with sensors gathering data to ensure safe and efficient operations. AI methods for time series modeling hold great potential to convert this data into insights, but computing at scale remains a challenge.

The CS-2 solves this by delivering cluster-scale compute with the programming ease of a single device, enabling researchers to quickly experiment with complex AI models and deploy the right solution in days-weeks instead of months-years.