Leibniz Supercomputing Centre

Leibniz Supercomputing Centre brings the first CS-2 to Europe!

Major US Financial Institution

Our CS-2 system delivered the compute performance of more than 120 AI-optimized GPUs for a major US financial institution, at almost half the energy consumption.

TotalEnergies Research

TotalEnergies is a global energy company that is working to accelerate multi-energy research. With a single CS-2, they demonstrated 100x improvement over general purpose processors for seismic modeling.

GlaxoSmithKline

GlaxoSmithKline is using AI to make better predictions in drug discovery and amassing data faster than ever before. They contacted us for a CS-1 system, enabling them to increase their model complexity while decreasing training time by 80x.

AstraZeneca

AstraZeneca is a global, science-led biopharmaceutical company that focuses on the discovery, development, and commercialization of prescription medicines in oncology, rare diseases, and biopharmaceuticals. With AI, they can iterate and experiment in real-time by running queries on hundreds of thousands of abstracts and research papers. With a CS-1 system, they are training models in just over two days that previously took more than two weeks.

Pittsburgh Supercomputing Center

Pittsburgh Supercomputing Center’s Neocortex supercomputer is designed to accelerate AI research in pursuit of science, discovery, and societal good. They reached out to us for two CS-1 systems, to accelerate and fundamentally transform how scientists develop and test their ideas.

EPCC

EPCC’s mission is to accelerate the effective use of novel computing throughout industry, academic, and commerce. They reached out to us for a CS-1 system to enable national-scale genomics research for public health initiatives in Scotland.

National Energy Technology Laboratory

GlaxoSmithKline is using AI to make better predictions in drug discovery and amassing data faster than ever before. They contacted us for a CS-1 system, enabling them to increase their model complexity while decreasing training time by 80x.

Lawrence Livermore National Laboratory

LLNL partnered with us to help blend traditional high performance computing (HPC) simulation and modeling workloads with artificial intelligence — achieving 18 million DNN inferences per second, at much lower cost than GPUs.

Argonne National Laboratory

ANL contacted us to explore how Cerebras Systems could accelerate their important cancer research with NIH and NCI – ultimately reducing training time from weeks to hours.

nference

nference uses transformer AI models to employ self-supervised learning from large volumes of unstructured data, translating vast amounts of health data into information that can be used to discover insights and drive research.