Principle:Sgl project Sglang Batch Text Generation
| Knowledge Sources | |
|---|---|
| Domains | LLM_Serving, Text_Generation, Inference |
| Last Updated | 2026-02-10 00:00 GMT |
Overview
A continuous batching mechanism that processes multiple text generation requests concurrently through a scheduler with RadixAttention-based KV cache management.
Description
Batch text generation is the process of submitting one or more prompts to an LLM and receiving generated completions. SGLang implements this with continuous batching — new requests can enter the batch while existing ones are still generating tokens. The system uses RadixAttention to share common prefixes across requests in a radix tree KV cache, avoiding redundant computation. The Engine accepts both synchronous (blocking) and asynchronous (non-blocking) generation modes, and supports streaming output via an iterator interface.
Usage
Use batch text generation for any offline inference workload — processing datasets, generating training data, evaluation benchmarks, or any scenario where you have a collection of prompts to process without real-time latency constraints.
Theoretical Basis
Continuous batching differs from static batching by allowing dynamic insertion and removal of requests:
- Requests can join the batch at any scheduler iteration
- Completed requests are removed immediately, freeing KV cache slots
- This maximizes GPU utilization compared to padding-based static batching
RadixAttention organizes KV cache entries in a radix tree (prefix tree):
- Common prompt prefixes are stored once and shared
- Cache eviction follows LRU (least recently used) policy
- Prefix sharing provides significant speedup for similar prompts