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New course: Build LLM applications that respond to user requests quickly by running on hardware designed for fast inference. This short course was built with @Cerebras and taught by @zhennydez, @duerr_seb, and @MilksandMatcha.<br><br>When a model generates text, much of the time is spent moving its weights out of memory and into the compute units. Inference-optimized hardware minimizes that movement, making token generation several times faster than on a typical GPU setup. In this course, the hardware you'll use is Cerebras' Wafer-Scale Engine, which is designed for fast inference by keeping the model's weights close to the compute units.<br><br>Fast inference makes lengthy agentic workflows go faster, and also unlocks latency-sensitive, real-time applications like live translation and voice agents.<br><br>Skills you'll gain:<br>- Compare how GPUs, TPUs, and Cerebras' Wafer-Scale Engine each handle the memory-to-compute bottleneck<br>- Build real-time applications powered by fast inference, including personalizing a webpage and running a multi-step workflow to analyze market signals<br>- Adopt concrete habits for agentic coding with fast inference, keeping your sessions focused and steering the model more effectively<br><br>My teams use Cerebras for several applications that are latency sensitive. Join and build LLM applications that respond quickly:<br>https://www.deeplearning.ai/courses/fast-llm-inference-with-cerebras<br><video width="1920" height="1080" src="https://video.twimg.com/amplify_video/2078144281685233664/vid/avc1/1920x1080/-jrErZb3PYjGtzPj.mp4?tag=29" controls="controls" poster="https://pbs.twimg.com/amplify_video_thumb/2078144281685233664/img/XjzBuLcIvBVuBSOk.jpg"></video>