Inference

In recent years, Large Language Models (LLMs) have achieved revolutionary breakthroughs in fields such as natural language processing, code generation, and even multimodal interaction. However, the powerful capabilities of these models come at the cost of enormous computational and memory overhead, especially during the inference stage. Efficiently deploying and running these models, which have billions or even trillions of parameters, has become a core challenge in scaling LLM technology for real-world applications. ...

As the parameters of Large Language Models (LLMs) continue to grow, deploying and serving these models presents significant challenges. vLLM is an open-source library designed for fast, convenient, and cost-effective LLM inference and online serving. Its core lies in the PagedAttention algorithm, which efficiently manages the KV Cache in the attention mechanism. Evaluation Metrics To evaluate the performance of LLM inference and serving engines, we primarily focus on the following metrics: ...