Sglang

SGLang is a community skill for serving and programming large language models using the SGLang framework, covering model serving, structured generation, batched inference, prompt programming, and performance optimization for LLM deployment.

What Is This?

Overview

SGLang provides tools for efficiently serving and interacting with large language models through a specialized runtime. It covers model serving that loads and runs LLMs with optimized memory management and request scheduling, structured generation that constrains model output to follow specific formats like JSON or regex patterns, batched inference that processes multiple requests simultaneously for throughput optimization, prompt programming that composes complex multi-turn interactions with control flow, and performance optimization that tunes serving parameters for latency and throughput. The skill helps developers deploy LLMs efficiently in both research and production environments.

Who Should Use This

This skill serves ML engineers deploying language models to production, researchers running inference experiments at scale, and developers building applications that require structured LLM output or high-throughput batch processing pipelines.

Why Use It?

Problems It Solves

Naive LLM serving wastes GPU memory with inefficient KV-cache management. Generating structured output like JSON requires post-processing that may produce invalid formats. Sequential request processing underutilizes GPU capacity for batch workloads. Complex prompt chains with branching logic are difficult to express with simple API calls. SGLang addresses each of these challenges through purpose-built abstractions.

Core Highlights

Model server optimizes memory with RadixAttention caching. Structured generator constrains output to JSON, regex, or grammar formats. Batch processor handles concurrent requests with continuous batching. Prompt programmer composes multi-step LLM interactions with native Python control flow.

How to Use It?

Basic Usage

import sglang as sgl

@sgl.function
def classify(
  s, text: str
):
  s += sgl.system(
    'Classify the '
    'sentiment as '
    'positive, negative, '
    'or neutral.')
  s += sgl.user(text)
  s += sgl.assistant(
    sgl.gen(
      'result',
      choices=[
        'positive',
        'negative',
        'neutral']))

@sgl.function
def extract_json(
  s, text: str
):
  s += sgl.system(
    'Extract entities '
    'as JSON.')
  s += sgl.user(text)
  s += sgl.assistant(
    sgl.gen(
      'output',
      max_tokens=256,
      regex=(
        r'\\{[^}]+\\}')))

runtime = sgl.Runtime(
  model_path=(
    'meta-llama/'
    'Llama-3-8B-Instruct'),
  tp_size=1)
sgl.set_default_backend(
  runtime)

result = classify.run(
  text='Great product')
print(result['result'])

data = extract_json.run(
  text='John works at '
    'Acme in NYC')
print(data['output'])
runtime.shutdown()

Real-World Examples

import sglang as sgl

@sgl.function
def summarize(
  s, doc: str
):
  s += sgl.system(
    'Summarize in '
    'one sentence.')
  s += sgl.user(doc)
  s += sgl.assistant(
    sgl.gen(
      'summary',
      max_tokens=100))

class BatchProcessor:
  def __init__(
    self,
    model: str,
    tp_size: int = 1
  ):
    self.runtime = (
      sgl.Runtime(
        model_path=model,
        tp_size=tp_size))
    sgl.set_default_backend(
      self.runtime)

  def process(
    self,
    documents: list[str]
  ) -> list[str]:
    states = (
      summarize.run_batch(
        [{'doc': d}
         for d in
         documents]))
    return [
      s['summary']
      for s in states]

  def shutdown(self):
    self.runtime\
      .shutdown()

proc = BatchProcessor(
  'meta-llama/'
  'Llama-3-8B-Instruct')
docs = [
  'Article about AI...',
  'Report on climate...',
  'Study of markets...']
summaries = (
  proc.process(docs))
for s in summaries:
  print(s)
proc.shutdown()

Advanced Tips

Use RadixAttention to cache shared prompt prefixes across requests that share system messages, which reduces redundant computation and lowers latency for repeated prompt patterns. Enable continuous batching to maximize GPU utilization during concurrent request processing. Combine constrained decoding with regex patterns for reliable structured output generation. When serving larger models, increase tp_size to distribute weights across multiple GPUs using tensor parallelism.

When to Use It?

Use Cases

Deploy a language model server with constrained JSON output for structured data extraction. Run batch summarization across thousands of documents with throughput-optimized inference. Build a multi-step prompt pipeline that classifies and then extracts entities from text.

Related Topics

LLM serving, model inference, structured generation, batch processing, SGLang, vLLM, and prompt engineering.

Important Notes

Requirements

SGLang Python package installed with compatible PyTorch and CUDA versions for GPU inference. GPU with sufficient VRAM for loading the target language model weights into memory. Model weights downloaded locally or accessible from a supported model repository for serving.

Usage Recommendations

Do: use constrained generation for structured outputs to avoid format parsing failures. Batch requests when possible to maximize GPU throughput. Monitor memory usage when serving large models to prevent out-of-memory failures.

Don't: serve models larger than GPU memory without tensor parallelism configuration. Ignore request timeouts since long generations can block batch processing. Use unconstrained generation for JSON output since models may produce invalid formats.

Limitations

SGLang requires compatible GPU hardware and CUDA drivers for model serving. Constrained generation adds decoding overhead that slightly increases latency. Model serving performance depends on GPU memory and compute capacity relative to model size.