Fast LLM Inference with Cerebras

When a language model generates text, much of the time goes to moving the model's weights from memory to the compute units. Inference-optimized hardware keeps those weights close to compute, generating tokens several times faster than a typical GPU setup. That speed changes both what you can build and how you build it.

This course, built in partnership with Cerebras and taught by Zhenwei Gao, Sebastian Duerr, and Sarah Chieng of Cerebras, shows you how to build LLM applications that respond in real time on Cerebras' Wafer-Scale Engine (WSE-3). It's a chip large enough to hold a model's weights on-chip, right next to the compute units. You'll run fast inference and see where that speed matters most: latency-sensitive use cases like live personalization and real-time multi-tool workflows.

Fast inference also simplifies your code. When the model responds before a user really notices, you can drop the workarounds built to hide slow responses, like loading spinners or streaming text out a few words at a time, and just call the model directly. You'll see the same shift reshape agentic coding: validating between steps instead of at the end, so you catch issues early and produce cleaner code.

In detail, you'll:

• Examine how inference speeds have evolved across hardware generations, then run fast inference to experience how it transforms application design.
• Compare how GPUs, TPUs, and Cerebras' Wafer-Scale Engine handle the memory-to-compute bottleneck, and why keeping weights on-chip minimizes data movement.
• Rethink how you design applications around fast inference, calling the model directly instead of engineering around slow responses.
• Build a real-time 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.

By the end, you'll know how to build the kind of responsive, latency-sensitive applications that fast inference makes possible.

This course is for developers and AI engineers who want to build faster, more responsive LLM applications and agentic systems. Basic familiarity with Python and calling an LLM API will help you get the most out of the hands-on labs.