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📝 Structured Generation Language (SGLang)

Description​

< What is it? >​

Structured Generation Language (SGLang) is an open-source, high-performance inference and serving framework for large language models (LLMs) and multimodal models. It loads a trained model and exposes an API that an application can call for generation, chat, embeddings, or structured outputs.

SGLang is primarily a serving runtime. It does not train or fine-tune model weights; it helps run an already trained model efficiently on one accelerator or across a larger deployment.

Application → SGLang server → scheduler and KV cache → GPU model → response

Key points​

< Why use it? >​

CapabilityWhy it matters
OpenAI-compatible APIAn application that already calls an OpenAI-style chat-completions API can often point its client at an SGLang server instead.
Continuous batchingThe server combines compatible requests, helping a GPU serve many users efficiently.
Prefix cachingShared prompt prefixes can reuse key–value (KV) cache work instead of recomputing identical tokens.
Structured outputsA JSON schema, regular expression, or grammar can constrain the generated format.
Serving featuresSGLang supports deployment choices such as quantization, parallelism, streaming, and multi-LoRA serving.

Performance still depends on the model, hardware, context length, request mix, and server configuration. Benchmark the workload you actually intend to serve.

< Simple example: launch a local server and chat >​

After installing SGLang and making a compatible model available, launch a server:

python3 -m sglang.launch_server \
  --model-path qwen/qwen2.5-0.5b-instruct \
  --host 127.0.0.1

It exposes an OpenAI-compatible endpoint. A Python application can call it with the standard OpenAI client:

from openai import OpenAI

client = OpenAI(
    base_url="http://127.0.0.1:30000/v1",
    api_key="None",  # no real provider key for this local example
)

response = client.chat.completions.create(
    model="qwen/qwen2.5-0.5b-instruct",
    messages=[
        {"role": "user", "content": "List three countries and their capitals."}
    ],
    temperature=0,
    max_tokens=64,
)

print(response.choices[0].message.content)

< Simple example: require valid JSON >​

For an application that needs machine-readable data, constrain the response to a schema instead of asking for “JSON” only:

from pydantic import BaseModel


class CapitalInfo(BaseModel):
    name: str
    population: int


response = client.chat.completions.create(
    model="qwen/qwen2.5-0.5b-instruct",
    messages=[
        {"role": "user", "content": "Give information about France's capital."}
    ],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "capital_info",
            "schema": CapitalInfo.model_json_schema(),
        },
    },
)

capital = CapitalInfo.model_validate_json(
    response.choices[0].message.content
)

The schema constrains the output structure; application validation still helps handle model quality, missing facts, and business-specific rules.

< When it is a good fit >​

  • You want to self-host an LLM or vision-language model behind a production API.
  • Many requests share a long system prompt, retrieved context, or conversation prefix.
  • Your application needs streaming responses or reliably structured results.
  • You need to measure serving throughput, time to first token, and latency under realistic load.

Comparison​

TechnologyPrimary roleTypical output or artifact
PyTorchBuild, train, and evaluate modelsModel code and learned weights
SGLangServe LLMs and multimodal models efficientlyAn API server that generates model responses
TensorRTOptimize and run deep-learning inference on NVIDIA GPUsAn optimized inference engine

Reference​