Sglang Guide

---
name: sglang-guide
description: Serve LLMs fast with SGLang — structured generation, radix attention caching, and high-throughput inference runtimes.
category: ai-research
---

## Overview

SGLang is a serving framework for large language models that combines a fast
runtime with a structured-generation frontend language. On the backend, its
headline innovation is RadixAttention — a radix-tree-based KV cache that reuses
computation across requests sharing prefixes (system prompts, few-shot examples,
conversation history), dramatically raising throughput for workloads with common
prefixes. On the frontend, SGLang's language lets you express constrained
generation (choices, regex, JSON schemas) and multi-step programs that run
efficiently against the runtime.

Use SGLang when you're self-hosting open models and throughput matters: many
concurrent requests, long shared prefixes, or structured outputs at scale. It's
an inference-engine choice (like vLLM or TensorRT-LLM) with the extra benefit of
a programming model for structured generation built in.

The mental model: SGLang treats your serving workload as programs with shared
structure, and exploits that structure — prefix reuse in the cache, constraint
awareness in the scheduler — to serve more requests per GPU.

## When to use

- Self-hosting open models (Llama, Qwen, DeepSeek, etc.) with high request
  concurrency.
- Workloads with large shared prefixes: same system prompt across requests,
  few-shot templates, multi-turn conversations, agent tool loops.
- Structured generation at scale: JSON outputs, constrained choices, regex
  formats — expressed in SGLang's frontend language.
- Multi-step LLM programs (generate → verify → regenerate) where the runtime can
  optimize the whole program.
- Research on serving efficiency, caching, or constrained decoding.
- Cost-per-token optimization for open-model deployments.

## Core concepts

- **RadixAttention / prefix caching**: the KV cache is organized as a radix tree,
  so requests sharing a prefix reuse its computed key-values. The win scales with
  prefix length and sharing — measure hit rates on your workload.
- **Structured generation DSL**: express outputs as `gen` with choices, regexes,
  or JSON schemas, composed into programs with control flow. Constraints execute
  against the runtime efficiently (no naive retry loops).
- **Continuous batching**: requests join and leave the batch dynamically as they
  finish, keeping the GPU saturated. Standard in modern servers; SGLang's
  implementation is tuned around its cache design.
- **Cache-aware scheduling**: the scheduler considers cache locality when ordering
  requests — grouping requests with shared prefixes to maximize reuse. This is
  where the radix design pays off beyond raw caching.
- **Multi-step programs**: the frontend language supports branching, loops, and
  parallel generation (`fork`) within one program, with the runtime optimizing
  across steps (e.g., shared prefixes between branches).
- **OpenAI-compatible API**: serve with a familiar `/v1` interface so existing
  clients work unchanged; the SGLang-specific features are available through its
  own client/language.
- **Quantization support**: run quantized models to fit larger models or higher
  concurrency per GPU. Match quantization to your quality bar with evals.
- **Disaggregated / distributed serving**: options for splitting prefill and
  decode across instances and for tensor/pipeline parallelism on multi-GPU
  setups.

## Practical workflow

1. **Profile your workload's prefix sharing.** Estimate how much of each request
   is shared prefix (system prompt, examples, history). High sharing → SGLang's
   cache design wins big; fully independent short prompts → less advantage.
2. **Deploy the server.** Container-based deployment with your model weights;
   configure parallelism (tensor parallel across GPUs) and quantization to fit
   your hardware and latency targets.
3. **Benchmark throughput and latency.** Measure tokens/sec, time-to-first-token,
   and inter-token latency at your target concurrency. Tune max batch/token
   budgets against your SLOs.
4. **Express structured outputs in the DSL.** Replace "output JSON" prompts with
   schema-constrained `gen` calls; replace retry loops with constrained
   generation. Measure the quality and cost delta.
5. **Monitor cache hit rates.** Track prefix cache hits — a low hit rate means
   your workload doesn't share structure, or your prefix construction is
   inconsistent (e.g., nondeterministic prompt assembly defeating the cache).
6. **Stabilize prompt construction.** Build prompts deterministically — same
   logical prefix should produce byte-identical prefix strings, or the cache
   can't match them.
7. **Load-test before committing.** Soak-test at peak concurrency; watch for
   latency cliffs, OOMs, and cache eviction churn under sustained load.

Checklist for an SGLang deployment:
- Prefix sharing quantified; cache hit rates monitored.
- Throughput/latency benchmarked at target concurrency.
- Structured outputs expressed as constraints, not prompts.
- Prompt construction deterministic for cache matching.
- Quantization quality validated against the full-precision baseline.

## Common pitfalls

- **Nondeterministic prefixes.** Timestamps, random example ordering, or user IDs
  embedded early in the prompt defeat prefix caching silently. Keep shared
  content byte-identical and put variable content late.
- **Expecting wins on unshared workloads.** If every request is a unique short
  prompt, RadixAttention adds little — SGLang is still a fine server, but the
  headline benefit needs shared structure.
- **Ignoring time-to-first-token.** Throughput optimizations can trade TTFT for
  tokens/sec. If your UX needs fast first tokens (chat), tune for it explicitly.
- **Constraint overuse.** Constraining every generation adds overhead; use
  constraints where structure is required, plain generation elsewhere.
- **Undersized GPU memory planning.** KV cache sizing interacts with batch size
  and sequence length. Plan memory for your p99 sequence lengths, not the mean.
- **No evals on quantized models.** Quantization can subtly degrade quality on
  your specific task. Evaluate the quantized model on your task before
  production.
- **Treating the DSL as required.** You can use SGLang purely as an
  OpenAI-compatible server. Adopt the frontend language where it helps; don't
  rewrite working clients unnecessarily.
- **Skipping soak tests.** Serving looks fine at 10 concurrent requests and falls
  over at 200. Load-test at realistic peaks plus headroom.