Solution Brief

Cerebras CS-2 With WEKA Data Platform for AI

Deep learning has emerged as one of the most important computational workloads of our generation. Its applications are widespread and growing. But deep learning is profoundly computationally intensive. Between 2015 and 2020, the compute used to train the largest models increased by 300,000x. In other words, AI compute demand is doubling every 3.5 months. Because of this voracious demand, AI is constrained by the availability of compute; not by applications or ideas. Testing a single new hypothesis—training a new model—can take weeks or months and can cost hundreds of thousands of dollars in compute time. This is a significant drag on the pace of innovation, and many important ideas are ignored simply because they take too long to test.