Andrew Feldman, co-founder and CEO of Cerebras gives the best explanation of why Cerebras' wafer-scale architecture is 2,500X faster than a GPU during LLM inference.

During inference, there are 2 stages:
- pre-fill, where the model first processes the user's prompt, and
- decode, where it generates the answer 1 token at a time in sequence.

During that sequencial Decode phase, before each token is calculated the model weights have to be moved from memory into compute.

On a GPU those weights are moved from HBM, while Cerebras keeps them in much faster SRAM spread across its very large wafer-scale processor, so the memory-to-compute movement that must happen for every token is about 2,500× faster

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From The MAD Podcast with Matt Turck and Cerebras YouTube channel, (link in comment)