Tests showed that vLLM can increase the throughput of LLMs by 2 to 4 times, especially with longer sequences and complex decoding algorithms. When compared to systems like FasterTransformer and Orca, vLLM demonstrated superior performance, processing twice the number of requests in chatbot applications. The PagedAttention algorithm and the vLLM serving system provide an efficient solution for serving LLMs by applying principles from operating system memory management.
Key takeaways:
- Researchers have introduced a new algorithm called PagedAttention, which is inspired by the virtual memory and paging systems of operating systems and aims to efficiently manage the key-value cache memory in Large Language Models (LLMs).
- PagedAttention partitions the KV cache of a request into blocks stored in non-contiguous memory spaces, mirroring techniques used by operating systems for memory management.
- The vLLM serving system was developed based on PagedAttention, which manages KV cache memory efficiently, reduces wastage, and improves throughput. It can increase the throughput of LLMs by 2 to 4 times, especially with longer sequences and intricate decoding algorithms.
- In tests against systems like FasterTransformer and Orca, vLLM showed improved performance, particularly with datasets of longer sequences. In applications like chatbots, vLLM processed twice the number of requests compared to Orca benchmarks.