The Fastest AI. Easy to Use.
We’ve built the fastest AI accelerator, based on the largest processor in the industry, and made it easy to use. With Cerebras, blazing fast training, ultra low latency inference, and record-breaking time-to-solution enable you to achieve your most ambitious AI goals.
Go Ahead – Reduce the Cost of Curiosity.
The CS-2, The Fastest AI Accelerator in the world
Purpose built for AI, the CS-2 replaces an entire cluster of graphics processing units (GPUs). Gone are the challenges of parallel programming, distributed training, and cluster management. From chip to system to software – every aspect of the CS-2 is optimized to accelerate and to simplify AI work. The CS-2 produces answers in less time.
Wafer Scale Engine: The Largest Chip Ever Built
The Wafer Scale Engine (WSE-2) is the largest chip ever built and powers the CS-2. The WSE-2 is 56 times larger than the largest GPU, has 123 times more compute cores, and 1000 times more high performance on-chip memory. The only wafer scale processor ever produced, it contains 2.6 trillion transistors, 850,000 AI-optimized cores, and 40 gigabytes of high performance on-wafer memory all aimed at accelerating your AI work.
2.6 Trillion Transistors
Unlock the Full Potential of AI to Accelerate Your Business
Innovate to do what hasn’t been done before.
Invent and build new AI applications with a computing platform that was built from the ground up for this work. Develop new neural network architectures, ML methods, and algorithms not practical or possible on legacy, general purpose hardware.
Reduce the cost of curiosity with game-changing performance.
Getting to business impact means asking and answering more questions in a single unit of time. Instead of weeks or months, reduce training time to days or even hours. Reduce latency in inference from milliseconds to microseconds.
Harness cluster-scale performance with the programming ease of a single node.
Our software stack meets you where you are, enabling easy programming with ML frameworks like TensorFlow and PyTorch. This means faster time to solution with less time spent engineering and optimizing distributed implementations of your model for a cluster of small, traditional processors.