Today, the fundamental limiting factor in running LLMs is not the AI. It is the distributed compute challenge of putting LLMs on thousands of GPUs, and the scarcity of the distributed compute expertise necessary to do so. Ironically, what’s hard about big AI isn’t AI. It’s the distributed compute.
The key to the new Cerebras Wafer-Scale Cluster is the exclusive use of data parallelism to train. Data parallelism is the preferred approach for all AI work. However, data parallelism requires that all the calculations, including the largest matrix multiplications of the largest layer, fit on a single device, and that all the parameters fit in the device’s memory. Only the CS-2. and not GPUs or single ASICs, can achieve both characteristics for LLMs.the AI. It is the distributed compute.
We look forward to understanding the ROI of deploying large models on Cerebras, and, if it looks attractive, we expect Cerebras to become one of the few AI startups that can challenge NVIDIA’s lead in AI. A startup cannot displace NVIDIA with a platform that is just faster. It will take an enormous leap of innovation that demands a totally different approach. And Cerebras has one of the few approaches we have seen that has that potential.
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