Transformer models, particularly decoder-only architectures, are the foundation of advanced tools that operate by predicting the next token based on prior data. A precise understanding of the mechanics of these models requires examining four main pillars: the autoregressive generation process, the prefill and decode stages, key-value cache (KV Cache) management, and the analysis of memory consumption in this process.
In this regard, optimizing inference in artificial intelligence depends not only on a theoretical understanding of the model, but also on the proper management of memory resources during token processing. Techniques such as KV Cache significantly increase the model's response speed by reducing the computational load.