The first implementation of inference on the Wafer Scale Engine and utilized only a fraction of its peak bandwidth, compute, and IO capacity. Today’s release is the culmination of numerous software, hardware, and ML improvements we made to our stack to greatly improve the utilization and real-world performance of Cerebras Inference.
 
  We’ve re-written or optimized the most critical kernels such as MatMul, reduce/broadcast, element wise ops, and activations. Wafer IO has been streamlined to run asynchronously from compute. This release also implements speculative decoding, a widely used technique that uses a small model and large model in tandem to generate answers faster.

A big question is what they're using as their draft model; there's ways to do it losslessly, but they could also choose to trade off accuracy for a bigger increase in speed.

It seems they also support only a very short sequence length. (1k tokens)