Hello everyone, my name is heroesunly. Graduated from 985 University with a master's degree and now works as an algorithm researcher. He is keen on the research and application of machine learning algorithms. He won first place in the Alibaba Cloud Tianchi competition, second place in the CCF competition, and third place in the iFlytek competition. Possess multiple invention patents. Have unique insights into machine learning and deep learning. I have tutored several non-computer major students to find employment in the algorithm industry. I hope to grow and progress with you all.
This article mainly introduces the practical plan for deploying the large model inference acceleration framework vllm. I hope it will be helpful to students who are learning large language models.
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1 Introduction
vLLM is a Python-based LLM (Large Language Model) inference and service framework. Its main advantages include ease of use and high performance.
The specific advantages are as follows:
- Super service throughput
- Use PagedAttention to efficiently manage attention keys and values
- Continuous batching of incoming requests
- Optimized CUDA core
vLLM is flexible and easy to use