Cerebras designs AI computing solutions that are faster, more powerful, and easier to deploy than GPUs. Powered by our breakthrough Wafer-Scale Engine-3, the Cerebras CS-3 sytem clusters seamlessly to form the world’s most powerful AI supercomputers.

Train and fine-tune models ranging from 1 billion to 24 trillion parameters—far faster than the largest publicly available cloud GPU instances. With Cerebras Cloud, you’ll reduce training …

Mar 19, 2025 · This section presents a conceptual view of computing with the Cerebras architecture. Read this before you get into the details of how to write programs with the Cerebras SDK.

Jul 20, 2023 · Today Cerebras is excited to announce Condor Galaxy 1 (CG-1), a 4 exaFLOPS, 54 million core, 64-node AI supercomputer. Built in partnership with G42, the leading AI and …

At Cerebras, we address interesting challenges with passionate, collaborative teams in an environment with very little overhead. We also provide the essentials: premiere medical, dental, vision, and life insurance plans, generous vacation, 401k, and Group RRSP retirement plans and an inclusive, flexible work environment.

Sep 14, 2022 · The Cerebras Wafer-Scale Cluster delivers near-perfect linear scaling across hundreds of millions of AI-optimized compute cores while avoiding the pain of the distributed compute.

The Cerebras CS-2 is the fastest AI system in existence and it is powered by the largest processor ever built – the Cerebras Wafer-Scale Engine 2 (WSE-2), which is 56 times larger than the nearest competitor.

Mar 19, 2025 · This is the documentation for developing kernels for Cerebras system. Here you will find getting started guides, quickstarts, tutorials, code samples, release notes, and more.

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

Mar 19, 2025 · This series of tutorials serves as an introduction to bulding programs written in CSL using the Cerebras SDK. In each successive tutorial, we introduce additional language features, using a general matrix-vector product (GEMV) as our core computation.