Hello, I'm Shrijith Venkatramana. I'm building git-lrc, an AI code reviewer that runs on every commit. Star Us to help devs discover the project. Do give it a try and share your feedback for improving the product.
Large Language Models can generate surprisingly intelligent responses. But there's a hidden engineering challenge behind every answer:
LLMs generate text one token at a time. To predict each new token, a transformer model processes the entire sequence of tokens seen so far and uses its attention mechanism to determine which earlier tokens are most relevant for the next prediction. Naively, this means that when generating the 1,000th token, the model would need to repeatedly compute representations for the previous 999 tokens even though those tokens have not changed.
How do you generate the 1,000th token without repeatedly recomputing information for the previous 999 tokens over and over again?
If models had to recompute everything from scratch for every generated token, response times would be painfully slow and inference costs would explode.
