Because Cerebras handles large models poorly due to latency/bandwidth issues to main memory. See https://openai.com/index/introducing-gpt-5-3-codex-spark/ where its performance is significantly below that of the regular Codex 5.3, and can only handle a 128k text context window. For some use cases its great, but most would rather use a better, slower model.

In the future, they plan hybrid implementations, to be able to serve large models better, e.g.

"AWS. We signed a binding term sheet with Amazon Web Services for AWS to become the first hyperscaler to deploy Cerebras systems in its data centers. Deployment in AWS data centers will require us to meet strict standards for performance, scale, and reliability.Pursuant to the term sheet, we will create a co-designed, disaggregated inference-serving solution that will integrate AWS Trainium3 chips with Cerebras CS-3 systems, connected via high-bandwidth networking, to partition inference workloads across Trainium3 and CS-3. Each system will perform the type of computation at which it most excels. The approach is expected to deliver 5 times more token throughput in the same hardware footprint, at up to 15 times faster speeds compared to leading GPU-based solutions as benchmarked on leading open-source models."