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.
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.
Fast inference makes lengthy agentic workflows go faster, and also unlocks latency-sensitive, real-time applications like live translation and voice agents.
Skills you'll gain:
- Compare how GPUs, TPUs, and Cerebras' Wafer-Scale Engine each handle the memory-to-compute bottleneck
- Build real-time applications powered by fast inference, including personalizing a webpage and running a multi-step workflow to analyze market signals
- Adopt concrete habits for agentic coding with fast inference, keeping your sessions focused and steering the model more effectively
My teams use Cerebras for several applications that are latency sensitive. Join and build LLM applications that respond quickly:
deeplearning.ai/courses/fast…
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