Double Your LLM Inference Speed with One Line of Code | Cerebras Predicted Outputs

What if you could 2× your inference speed by changing just one line of code? In this interview, Ryan Loney, a Product Manager at Cerebras, walks us through Predicted Outputs — a new inference feature that lets LLMs generate code and documents dramatically faster by reusing parts of the output that are already known. By telling the model which sections of the response will stay the same, Predicted Outputs can reuse over 80% of tokens during generation — delivering major latency gains without changing your existing workflow. In this video, you’ll learn: How Predicted Outputs work under the hood Why a single parameter change can unlock 2× faster inference Real usage metrics showing token reuse in action Best use cases: code refactors, full-file edits, inline completions, grammar fixes, and structured text What happens when your prediction is wrong (spoiler: the model just regenerates the mismatched parts) No extra logic. No new framework. Just faster inference. If your output is mostly known, this is one of the easiest performance wins you can get.