vLLM: High-Throughput Serving for LLMs

Deploying Large Language Models (LLMs) in production requires more than just raw power; it requires extreme efficiency. vLLM is a high-throughput serving engine that uses PagedAttention to maximize GPU memory utilization.

PagedAttention Technology

Traditional serving methods waste up to 60-80% of GPU memory due to fragmentation and over-reservation. vLLM’s PagedAttention allows for near-perfect memory usage, enabling much larger batch sizes and significantly increasing the number of requests a single GPU can handle simultaneously.

Continuous Batching

By implementing continuous batching, vLLM ensures that new requests can be added to the processing queue without waiting for existing ones to finish. This leads to much lower latency and higher throughput, making it the industry standard for high-performance AI inference.