What happens between entering the prompt and seeing the first word appear (shbhmrzd.github.io)

A new post has detailed the intricacies of how large language models (LLMs) generate text from user prompts, illuminating the processes behind token generation. The article emphasizes that, unlike the parallel token predictions made during training, inference in LLMs occurs sequentially—each token is generated one at a time, which inherently slows down response times. The process begins with prompt tokens being transformed into IDs, followed by an autoregressive step that samples one token based on the logits produced by the model after a forward pass. The introduction of a Key-Value (KV) cache significantly optimizes this process by storing computed vectors, preventing redundancy in recalculating Key and Value pairs at every step, thereby reducing the overall computational workload and improving efficiency. This discussion is crucial for the AI/ML community as it deepens the understanding of LLM mechanics and highlights areas for performance improvement. The implications of this work are particularly significant as models grow in context length and user interactions increase, where efficient memory usage and reduced latency are paramount. The KV cache design, coupled with an innovative approach known as Grouped Query Attention (GQA), allows models to maintain performance even under heavy load, marking a notable advancement in resource management for large-scale LLMs.