Researchers at TNGTech have proposed an optimization technique to improve the performance of large language models (LLMs) in concurrent requests. The method, called 'Prefill and Decode', involves preloading model weights for multiple requests simultaneously, reducing latency and increasing throughput. This approach is particularly relevant in applications where LLMs are used to handle a high volume of queries or requests, such as chatbots or question-answering systems. --- Why it matters: This matters because it can significantly improve the efficiency and scalability of AI-powered applications that rely on LLMs, allowing them to handle more concurrent requests without sacrificing performance. Source: https://huggingface.co/blog/tngtech/llm-performance-prefill-decode-concurrent-requests