Optimizing Large Language Model Inference: A Deep Dive into KV Cache Computational Savings

This article explores the computational savings offered by KV cache in the context of Transformer-based Large Language Model (LLM) inference. By analyzing the theoretical performance gains, the author provides valuable insights into optimizing the inference process, leading to potentially faster and more efficient LLMs.

• •The article focuses on calculating the computational savings achieved through KV cache implementation during LLM inference.
• •It provides a theoretical analysis of the performance improvements when generating one token after 'T' tokens are already generated.
• •The study uses the GPT-2 model as a reference point for understanding the practical application of the concepts.