Fast inference changes what you can build

Description

Learn more: https://bit.ly/3Tg20Zd When an LLM generates text, much of the time goes to moving the model's weights from memory to the compute units, not to the math itself. On a GPU those weights sit off-chip, and a large model may be split across several chips, so data travels back and forth before computation can even start. That delay compounds. An agentic workflow might generate hundreds of thousands of tokens before it returns anything to the user. In our new short course, Fast LLM Inference with Cerebras, built in partnership with Cerebras and taught by Zhenwei Gao, Sebastian Duerr, and Sarah Chieng, you'll build applications on hardware designed to remove that bottleneck. Cerebras' Wafer-Scale Engine (WSE-3) is a single chip about the size of a large dining plate, big enough to hold a model's weights on-chip, right next to the compute units. Tokens come out several times faster than on a typical GPU setup, and that changes both what you can build and how you build it. What you can build: real-time, latency-sensitive applications that were impractical when every response carried a delay, like live translation and voice agents. How you build it: once a response lands before the user notices the wait, you can drop the loading spinners, async queues, and precomputed results, and just call the model directly. In this course, you'll learn how to: - Compare how GPUs, TPUs, and the Wafer-Scale Engine handle the memory-to-compute bottleneck, and why keeping weights on-chip minimizes data movement. - Build a live personalization use case that adapts a webpage to users as they interact with it. - Assemble a real-time, multi-tool workflow that runs live analysis of market signals in one fast response. - Adopt concrete habits for multi-agent coding with Codex, validating between tasks to catch issues early and ship cleaner code. Enroll now: https://bit.ly/3Tg20Zd