Principle:LLMBook zh LLMBook zh github io High Throughput LLM Inference
| Knowledge Sources | |
|---|---|
| Domains | NLP, Inference, Systems |
| Last Updated | 2026-02-08 00:00 GMT |
Overview
An inference optimization approach that uses continuous batching and paged attention to maximize throughput when serving large language models.
Description
High Throughput LLM Inference addresses the challenge of efficiently serving LLMs in production. Traditional inference processes one request at a time, leading to GPU underutilization. High-throughput inference engines like vLLM use two key innovations: (1) continuous batching, which dynamically adds new requests to the current batch as previous ones complete, and (2) PagedAttention, which manages KV-cache memory like virtual memory pages to reduce fragmentation.
Usage
Use this principle when deploying LLMs for batch inference or serving multiple concurrent requests. It is the standard approach for any production LLM deployment where throughput and latency matter.
Theoretical Basis
Key components:
- Continuous Batching: Instead of waiting for all requests in a batch to finish, new requests are added as slots become available.
- PagedAttention: KV-cache is stored in non-contiguous memory blocks (pages), avoiding wasteful pre-allocation.
- Sampling: Configurable decoding strategies (greedy, top-k, top-p, temperature-based).