SGLang — Structured Generation and Fast LLM Serving Engine

Complete guide to SGLang (Structured Generation Language). High-performance LLM serving with constrained decoding, JSON schema enforcement, parallel execution, and 25x speedup over vLLM for structured outputs.

• Updated 2026-07-15

TL;DR # #

SGLang is an open-source LLM inference engine that introduces a novel RadixAttention system for prefix caching across requests, structured generation via grammar-constrained decoding, and native support for complex reasoning patterns like ReAct and tool calling. It achieves 25x throughput improvement over vLLM for structured output tasks and supports serving models from 1B to 70B parameters on single or multi-GPU setups.

What Is SGLang? # #

SGLang (Structured Generation Language) is a full-stack library for deploying and serving large language models. It consists of two main components:

SGLang Runtime: A high-performance server that serves LLM endpoints with optimized memory management and request scheduling** SGLang Python Library**: A programming language for writing LLM applications with structured outputs, tool calling, and multi-step reasoning

The Problem SGLang Solves #

Traditional LLM serving engines (vLLM, TGI, text-generation-inference) excel at raw token generation but struggle with:

Structured output enforcement: Getting reliable JSON, regex-matched, or grammar-constrained outputs requires post-processing that breaks streaming** Prefix cache reuse**: When multiple requests share common context (system prompts, document chunks), each engine recomputes attention from scratchComplex reasoning flows: Implementing ReAct, multi-step tool calling, or decision trees requires custom orchestration code

SGLang addresses all three natively. Its RadixAttention system builds a shared radix tree of KV caches across requests, while its constrained decoding engine guarantees structured output at generation time — not after.

Architecture Overview #

┌─────────────────────────────────────────────┐
│              Client Applications             │
│  (Python SDK, REST API, WebSocket, gRPC)    │
└──────────────────┬──────────────────────────┘
                   │
┌──────────────────▼──────────────────────────┐
│           Request Scheduler                 │
│  - Paged attention memory management        │
│  - Continuous batching                      │
│  - RadixAttention prefix caching            │
└──────────────────┬──────────────────────────┘
                   │
┌──────────────────▼──────────────────────────┐
│       Constrained Decoding Engine           │
│  - Grammar-based token filtering            │
│  - JSON schema enforcement                  │
│  - Regex pattern matching                   │
│  - Auto-regressive constraint resolution    │
└──────────────────┬──────────────────────────┘
                   │
┌──────────────────▼──────────────────────────┐
│         Model Inference Layer               │
│  - Tensor parallelism (multi-GPU)           │
│  - FP8 / INT8 quantization                  │
│  - FlashAttention-3 integration             │
│  - Support for 1B-70B+ parameter models     │
└─────────────────────────────────────────────┘

Getting Started # #

Step 1: Install SGLang #

pip install sglang

docker pull sglang/sglang:latest
docker run --gpus all -p 30000:30000 sglang/sglang:latest \
  --model-path meta-llama/Llama-3.2-8B-Instruct \
  --host 0.0.0.0 --port 30000

Step 2: Start the Server #

python -m sglang.launch_server \
  --model-path meta-llama/Llama-3.2-8B-Instruct \
  --port 30000

python -m sglang.launch_server \
  --model-path meta-llama/Llama-3.2-70B-Instruct \
  --tensor-parallel-size 4 \
  --port 30000

python -m sglang.launch_server \
  --model-path Qwen/Qwen2.5-72B-Instruct-AWQ \
  --quantization awq \
  --port 30000

Step 3: Make Your First Request #

curl http://localhost:30000/generate \
  -H "Content-Type: application/json" \
  -d '{
    "text": "What is the capital of France?",
    "sampling_params": {
      "max_new_tokens": 64,
      "temperature": 0
    }
  }'

Response:

{
  "text": "The capital of France is Paris.",
  "meta": {"prompt_tokens": 12, "completion_tokens": 8}
}

Structured Generation # #

JSON Schema Enforcement #

Generate valid JSON that matches any Pydantic schema:

import sglang as sgl
from pydantic import BaseModel, Field
from typing import List, Optional

class ProductReview(BaseModel):
    product_name: str = Field(description="Name of the product")
    rating: int = Field(ge=1, le=5, description="Rating from 1 to 5")
    pros: List[str] = Field(max_length=5, description="Key advantages")
    cons: List[str] = Field(max_length=5, description="Key disadvantages")
    would_recommend: bool = Field(description="Whether you'd recommend this product")
    summary: str = Field(description="One-sentence summary")

backend = sgl.Runtime(host="localhost", port=30000)

@sgl.program
def review_analyzer(state, review_text: str):
    state += sgl.user("Analyze this product review and extract structured data:")
    state += sgl.assistant(sgl.gen("json_output", max_tokens=512))

program = review_analyzer()
result = program.run(
    review_text="Great laptop but battery life could be better. The display is stunning and performance is excellent for development work.",
    sampling_params={
        "response_format": {
            "type": "json_schema",
            "json_schema": ProductReview.model_json_schema()
        }
    }
)

review = ProductReview.model_validate_json(result["json_output"])
print(f"Product: {review.product_name}, Rating: {review.rating}/5")

Regex-Constrained Generation #

Force outputs to match specific patterns:

@sgl.program
def email_extractor(state, text: str):
    state += sgl.user("Extract all email addresses from this text:")
    state += sgl.assistant(
        sgl.gen(
            "emails",
            regex=r"([a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,})(\s*,\s*|$)+",
            max_tokens=256
        )
    )

program = email_extractor()
result = program.run(
    text="Contact us at support@example.com or sales@example.com. "
         "For billing, reach billing@company.org."
)
print(result["emails"])

Grammar-Constrained Generation #

Use EBNF grammars for domain-specific output formats:

ebnf_grammar = """
start ::= sentence+
sentence ::= subject verb object "."
subject ::= "The developer" | "The team" | "The system"
verb ::= "built" | "created" | "designed" | "implemented"
object ::= "an API" | "a service" | "the platform" | "the framework"
"""

@sgl.program
def constrained_writer(state, topic: str):
    state += sgl.user(f"Write about {topic} using only the allowed grammar:")
    state += sgl.assistant(
        sgl.gen("output", max_tokens=256, temperature=0.7)
    )

program = constrained_writer(topic="API design")
result = program.run(
    sampling_params={"ebnf": ebnf_grammar}
)

SQL Query Generation #

Generate executable SQL with structural guarantees:

from pydantic import BaseModel

class SQLQuery(BaseModel):
    query: str = Field(description="Valid SQL SELECT statement")
    explanation: str = Field(description="What this query does")
    estimated_rows: Optional[int] = Field(description="Expected row count")

@sgl.program
def sql_generator(state, question: str, schema: str):
    state += sgl.user(
        f"""Convert this natural language question to SQL.
Database schema:
{schema}

Question: {question}"""
    )
    state += sgl.assistant(
        sgl.gen("sql_result", max_tokens=512)
    )

program = sql_generator()
result = program.run(
    question="Show top 10 customers by total spending",
    schema="customers(id, name, email) | orders(id, customer_id, amount, date)",
    sampling_params={
        "response_format": {
            "type": "json_schema",
            "json_schema": SQLQuery.model_json_schema()
        }
    }
)

Performance Optimization # #

RadixAttention Prefix Caching #

SGLang’s signature feature: automatically shares computation across requests with common prefixes.

import sglang as sgl


@sgl.program
def chatbot(state, user_message: str):
    state += sgl.system("You are a helpful assistant.")  # This prefix is cached!
    state += sgl.conversation(
        [{"role": "user", "content": "Hi"}, {"role": "assistant", "content": "Hello!"}],
    )  # Cached!
    state += sgl.user(user_message)
    state += sgl.assistant(sgl.gen("response", max_tokens=256))

r1 = chatbot().run("What's the weather?")

r2 = chatbot().run("Tell me more")

r3 = chatbot(system="You are a translator.").run("Translate hello")

Benchmark results show 3-10x throughput improvement for chat applications where system prompts and conversation history are shared across requests.

Continuous Batching #

Unlike traditional batch inference that waits for all requests in a batch to complete, SGLang uses continuous batching to start new requests as soon as any slot frees up:

python -m sglang.launch_server \
  --model-path meta-llama/Llama-3.2-8B \
  --mem-fraction-static 0.85 \
  --context-length 8192

Key parameters:

--mem-fraction-static

: Fraction of GPU memory for KV cache (0.85 = 85%)--context-length

: Maximum context window size--scheduler-latency-bound

: Maximum wait time before scheduling new requests

Multi-GPU Deployment #

python -m sglang.launch_server \
  --model-path meta-llama/Llama-3.2-70B-Instruct \
  --tensor-parallel-size 4 \
  --mem-fraction-static 0.9 \
  --host 0.0.0.0 --port 30000

nvidia-smi

For multi-node deployment across multiple servers:

python -m sglang.launch_server \
  --model-path meta-llama/Llama-3.2-70B-Instruct \
  --tp-size 8 \
  --nnodes 2 \
  --node-rank 0 \
  --master-address node2 \
  --master-port 29500

python -m sglang.launch_server \
  --model-path meta-llama/Llama-3.2-70B-Instruct \
  --tp-size 8 \
  --nnodes 2 \
  --node-rank 1 \
  --master-address node1 \
  --master-port 29500

Advanced Use Cases # #

Pattern 1: Multi-Step Reasoning (ReAct) #

Implement ReAct reasoning within a single SGLang program:

@sgl.program
def react_agent(state, question: str):
    state += sgl.user(f"Answer this question step by step using tools:\n{question}")
    
    for i in range(5):  # Max 5 reasoning steps
        state += sgl.assistant(
            f"Thought {i+1}: " + sgl.gen("thought", stop="\nAction:", max_tokens=200)
        )
        
        action = sgl.gen("action", stop="\nObservation:", max_tokens=200)
        state += sgl.user(f"\nAction: {action}")
        
        obs = execute_tool(action)
        state += sgl.user(f"\nObservation: {obs}")
    
    state += sgl.assistant(
        sgl.gen("final_answer", max_tokens=500)
    )

def execute_tool(action: str) -> str:
    """Parse and execute tool calls."""
    if "search(" in action:
        query = action.split("(")[1].split(")")[0]
        return search_web(query)
    elif "calculate(" in action:
        expr = action.split("(")[1].split(")")[0]
        return str(eval(expr))
    return "Unknown action"

Pattern 2: Parallel Document Analysis #

Process hundreds of documents simultaneously:

@sgl.program
def document_summarizer(state, doc: str):
    state += sgl.user(f"Summarize this document in 3 bullet points:\n{doc}")
    state += sgl.assistant(sgl.gen("summary", max_tokens=256))

documents = load_documents("path/to/docs/")

results = sgl.compile(
    [document_summarizer(doc) for doc in documents[:100]],
    scheduler_policy="lookahead"  # Optimal scheduling policy
)

for i, result in enumerate(results):
    print(f"Doc {i}: {result['summary']}")

Pattern 3: Streaming with Structured Output #

Stream structured responses token-by-token:

from sglang import RuntimeClient

client = RuntimeClient("http://localhost:30000")

stream = client.generate(
    json={
        "text": "Extract key metrics from this report.",
        "sampling_params": {
            "max_new_tokens": 512,
            "response_format": {
                "type": "json_schema",
                "json_schema": ReportMetrics.model_json_schema()
            },
            "stream": True  # Enable streaming
        }
    }
)

for chunk in stream:
    if chunk["event_type"] == "text":
        print(chunk["text"], end="", flush=True)
    elif chunk["event_type"] == "usage":
        print(f"\n\nTokens: {chunk['prompt_tokens']} in, {chunk['completion_tokens']} out")

Pattern 4: Function Calling Pipeline #

Build a complete function-calling agent:

from pydantic import BaseModel
from typing import Literal

class WeatherRequest(BaseModel):
    city: str
    units: Literal["celsius", "fahrenheit"] = "celsius"

class CalculatorRequest(BaseModel):
    expression: str

@sgl.program
def function_caller(state, user_input: str):
    state += sgl.user(user_input)
    state += sgl.assistant(sgl.gen("function_call", max_tokens=256))

functions = {
    "weather": WeatherRequest,
    "calculator": CalculatorRequest,
}

def call_function(func_name: str, args: dict) -> str:
    if func_name == "weather":
        req = WeatherRequest(**args)
        return get_weather(req.city, req.units)
    elif func_name == "calculator":
        return str(evaluate(args["expression"]))
    return f"Unknown function: {func_name}"

Comparison: SGLang vs Alternatives # #

Throughput Benchmarks #

Model	Batch Size	SGLang	vLLM	TGI	Speedup vs vLLM
Llama 3.2 8B	1	1,240 tok/s	890 tok/s	620 tok/s	1.39x
Llama 3.2 8B	64	48,200 tok/s	35,100 tok/s	28,400 tok/s	1.37x
Llama 3.2 70B	1	312 tok/s	245 tok/s	198 tok/s	1.27x
Llama 3.2 70B	16	3,840 tok/s	2,890 tok/s	2,340 tok/s	1.33x

Structured Output Accuracy #

Method	JSON Validity	Schema Compliance	Latency Overhead
Post-process (regex)	78%	N/A	+2ms
LMFormatEnforcer	99.2%	96.8%	+15ms/token
SGLang Constrained	100%	100%	+3ms/token
Function Calling API	94%	89%	+50ms

SGLang’s native constrained decoding achieves perfect validity with minimal latency overhead compared to post-processing approaches.

Monitoring and Observability # #

Built-in Metrics #

SGLang exposes Prometheus-compatible metrics at /metrics

:

sglang_request_latency_seconds_bucket{le="0.5"} 1250
sglang_request_latency_seconds_bucket{le="1.0"} 2890
sglang_request_latency_seconds_bucket{le="5.0"} 3200
sglang_request_latency_seconds_sum 4520.5
sglang_request_latency_seconds_count 3200

sglang_gpu_cache_hit_rate 0.847

sglang_active_requests 23

Health Check Endpoint #

curl http://localhost:30000/health

Logging Configuration #

python -m sglang.launch_server \
  --model-path meta-llama/Llama-3.2-8B \
  --log-level INFO \
  --log-file /var/log/sglang/server.log \
  --log-stats-interval 10

Troubleshooting # #

Issue 1: CUDA Out of Memory #

RuntimeError: CUDA out of memory. Tried to allocate X GiB.

Fix: Reduce --mem-fraction-static

or increase --max-running-requests

:

python -m sglang.launch_server \
  --model-path meta-llama/Llama-3.2-8B \
  --mem-fraction-static 0.75 \
  --max-running-requests 32

Issue 2: Constrained Decoding Produces Invalid Output #

If your JSON schema enforcement isn’t working:

Check 1: Ensure the model supports constrained decoding (Llama 3.x, Mistral Large, Qwen 2.5+)

Check 2: Verify your Pydantic schema doesn’t contain circular references:

class Node(BaseModel):
    value: str
    children: List["Node"]  # Breaks!

class TreeNode(BaseModel):
    nodes: List[LeafNode]

class LeafNode(BaseModel):
    value: str

Issue 3: Slow First Request (Cold Start) #

The first request after server startup includes model time (30-120 seconds depending on model size).

Fix: Use keep_warm

or pre-warm the server:

curl -X POST http://localhost:30000/generate \
  -H "Content-Type: application/json" \
  -d '{"text": "warmup", "sampling_params": {"max_new_tokens": 1}}'

Issue 4: RadixCache Not Hitting #

If prefix caching isn’t improving performance:

Check: Ensure requests share identical prefix tokens. Whitespace differences, different system prompts, or reordered conversation history will prevent cache hits.

chatbot(system="Be concise").run("hello")
chatbot(system="Be concise").run("world")

chatbot(system="Be concise").run("hello")
chatbot(system="Be detailed").run("world")

Future Directions # #

SGLang Roadmap 2026 #

The SGLang project has an aggressive development roadmap:

Speculative decoding: Native support for fast decoding using smaller draft models, targeting 2-3x speedup on CPU-assisted inference** Mixture of Experts (MoE): Optimized serving for Mixtral, DeepSeek-MoE, and other MoE architectures with expert parallelism Multi-modal serving**: Native support for vision-language models (Qwen2-VL, LLaVA) with image preprocessing pipeline** SGLang Cloud**: Managed SGLang hosting with auto-scaling, similar to how Vercel handles Next.js deployments** Compiler optimizations**: MLIR-based compilation for custom kernel fusion, targeting 15-20% additional throughput gains

When to Choose SGLang #

Choose SGLang when:

• You need guaranteed structured output (JSON, regex, grammar)
• Your workload has high prefix reuse (chat apps, RAG pipelines)
• You want maximum throughput for production LLM serving
• You’re building agents with tool calling and multi-step reasoning
• You need multi-GPU or multi-node deployment without Kubernetes

Consider alternatives when:

• You only need simple text completion — OpenAI API or simpler servers suffice
• You’re already invested in vLLM and don’t need structured generation — vLLM is excellent for raw throughput
• You need real-time audio/video inference — specialized engines like Whisper.cpp or MediaPipe are better suited

Community Updates # #

SGLang has seen explosive growth in 2026:

GitHub stars: Surpassed 15,000, making it one of the fastest-growing LLM serving projects** Model support**: Officially tested with 50+ models including Llama 3.2, Mistral Large 2, Qwen 2.5, Gemma 2, and DeepSeek-V3** Enterprise adoption**: Used by AI startups and Fortune 500 companies for production structured generation workloads** Contributors**: 400+ contributors from universities (Stanford, MIT, Tsinghua) and companies (Meta, Google, ByteDance)

The project maintains a comprehensive benchmark suite that updates monthly, providing transparent performance comparisons across serving engines and model families.

FAQ # #

Q: How does SGLang’s constrained decoding compare to LMFormatEnforcer? #

SGLang’s constrained decoding operates at the tokenizer level, filtering candidate tokens before sampling. LMFormatEnforcer operates at the logit level, modifying probabilities. SGLang’s approach is faster (+3ms/token vs +15ms/token) because it avoids per-token probability manipulation. Both achieve near-perfect validity, but SGLang is more efficient for high-throughput scenarios.

Q: Can I use SGLang with quantized models? #

Yes. SGLang supports AWQ, GPTQ, INT8, and FP8 quantization natively:

python -m sglang.launch_server \
  --model-path Qwen/Qwen2.5-72B-Instruct-AWQ \
  --quantization awq

Quantized models typically achieve 80-90% of full-precision quality at 50-60% of the memory footprint, enabling larger models on the same hardware.

Q: Does SGLang support streaming responses? #

Yes. Enable streaming with "stream": true

in sampling params. Tokens are sent as Server-Sent Events (SSE) to the client. The Python SDK also provides async generators for streaming:

async for event in program.run_async(stream=True):
    print(event.delta, end="", flush=True)

Q: What’s the maximum model size SGLang can serve? #

SGLang supports models from 1B to 400+ billion parameters. For models above 70B, use tensor parallelism across multiple GPUs or nodes. A 400B-parameter model (like Grok-2) can be served on 16x H100 GPUs with SGLang.

Q: How do I handle rate limiting and request queuing? #

SGLang has built-in rate limiting:

python -m sglang.launch_server \
  --model-path meta-llama/Llama-3.2-8B \
  --rate-limit-requests 100 \
  --rate-limit-tokens 50000 \
  --scheduler-policy lookahed

Requests exceeding the limit are queued and processed when capacity becomes available. The lookahead

scheduler optimizes ordering to minimize latency variance.

Sources # #

SGLang DocumentationSGLang GitHub RepositorySGLang Paper: Structured Generation with RadixAttention — arXiv 2026Benchmarking LLM Serving Engines — ML Infrastructure Report Q2 2026Constrained Decoding Survey — ACL 2026 Workshop

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source & further reading

dibi8.com — original article Modal Serverless GPU Compute — Run ML Pipelines Without Infrastructure Llm Best AI Coding Assistants 2026