# SGLang — Structured Generation and Fast LLM Serving Engine

> Source: <https://dibi8.com/resources/llm-frameworks/sglang-structured-generation-llm/>
> Published: 2026-07-15 00:00:00+00:00

# 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 [#](#tldr)

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? [#](#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 [#](#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 scratch**Complex 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 [#](#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 [#](#getting-started)

### Step 1: Install SGLang [#](#step-1-install-sglang)

```
# Install the Python library
pip install sglang

# Or use Docker for GPU acceleration
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 [#](#step-2-start-the-server)

```
# Serve a single model on one GPU
python -m sglang.launch_server \
  --model-path meta-llama/Llama-3.2-8B-Instruct \
  --port 30000

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

# With quantization for cost savings
python -m sglang.launch_server \
  --model-path Qwen/Qwen2.5-72B-Instruct-AWQ \
  --quantization awq \
  --port 30000
```

### Step 3: Make Your First Request [#](#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 [#](#structured-generation)

### JSON Schema Enforcement [#](#json-schema-enforcement)

Generate valid JSON that matches any Pydantic schema:

``` python
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")

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

# Create a stateful program
@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))

# Run with structured output
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()
        }
    }
)

# Result is guaranteed valid JSON matching the schema
review = ProductReview.model_validate_json(result["json_output"])
print(f"Product: {review.product_name}, Rating: {review.rating}/5")
```

### Regex-Constrained Generation [#](#regex-constrained-generation)

Force outputs to match specific patterns:

``` python
@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"])
# Output: "support@example.com, sales@example.com, billing@company.org."
```

### Grammar-Constrained Generation [#](#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 [#](#sql-query-generation)

Generate executable SQL with structural guarantees:

``` python
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 [#](#performance-optimization)

### RadixAttention Prefix Caching [#](#radixattention-prefix-caching)

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

``` python
import sglang as sgl

# Without RadixAttention: each request computes attention from scratch
# With RadixAttention: shared prefixes are cached and reused

@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))

# First request: full computation
r1 = chatbot().run("What's the weather?")

# Second request with same system prompt + conversation history:
# Only computes attention for the new user message
r2 = chatbot().run("Tell me more")

# Third request with different system prompt:
# No cache hit, full computation
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 [#](#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:

```
# Launch server with continuous batching enabled (default)
python -m sglang.launch_server \
  --model-path meta-llama/Llama-3.2-8B \
  --mem-fraction-static 0.85 \
  --context-length 8192

# Requests are processed continuously without waiting for batch completion
# This maximizes GPU utilization even with variable-length responses
```

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 [#](#multi-gpu-deployment)

```
# 4x A100-80GB for a 70B model
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

# Check GPU utilization
nvidia-smi
# Each GPU shows ~95% utilization during active inference
```

For multi-node deployment across multiple servers:

```
# Node 1 (rank 0)
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

# Node 2 (rank 1)
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 [#](#advanced-use-cases)

### Pattern 1: Multi-Step Reasoning (ReAct) [#](#pattern-1-multi-step-reasoning-react)

Implement ReAct reasoning within a single SGLang program:

``` python
@sgl.program
def react_agent(state, question: str):
    state += sgl.user(f"Answer this question step by step using tools:\n{question}")
    
    # Thought-Action-Observation loop
    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}")
        
        # Execute action (tool call)
        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 [#](#pattern-2-parallel-document-analysis)

Process hundreds of documents simultaneously:

``` python
@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))

# Process 100 documents in parallel
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 [#](#pattern-3-streaming-with-structured-output)

Stream structured responses token-by-token:

``` python
from sglang import RuntimeClient

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

# Stream JSON response
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 [#](#pattern-4-function-calling-pipeline)

Build a complete function-calling agent:

``` python
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))

# Define available functions
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 [#](#comparison-sglang-vs-alternatives)

### Throughput Benchmarks [#](#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 [#](#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 [#](#monitoring-and-observability)

### Built-in Metrics [#](#built-in-metrics)

SGLang exposes Prometheus-compatible metrics at `/metrics`

:

```
# HELP sglang_request_latency_seconds Request processing latency
# TYPE sglang_request_latency_seconds histogram
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

# HELP sglang_gpu_cache_hit_rate RadixAttention cache hit rate
sglang_gpu_cache_hit_rate 0.847

# HELP sglang_active_requests Currently active requests
sglang_active_requests 23
```

### Health Check Endpoint [#](#health-check-endpoint)

```
curl http://localhost:30000/health
# Returns: {"status": "ok", "gpu_memory_usage": "72%", "active_requests": 15}
```

### Logging Configuration [#](#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 [#](#troubleshooting)

### Issue 1: CUDA Out of Memory [#](#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 [#](#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:

```
# ❌ Circular reference breaks constrained decoding
class Node(BaseModel):
    value: str
    children: List["Node"]  # Breaks!

# ✅ Flatten to avoid recursion
class TreeNode(BaseModel):
    nodes: List[LeafNode]

class LeafNode(BaseModel):
    value: str
```

### Issue 3: Slow First Request (Cold Start) [#](#issue-3-slow-first-request-cold-start)

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

**Fix**: Use `keep_warm`

or pre-warm the server:

```
# Pre-load model with a dummy request
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 [#](#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.

```
# These WILL share cache (identical system prompt):
chatbot(system="Be concise").run("hello")
chatbot(system="Be concise").run("world")

# These WON'T share cache (different system prompt):
chatbot(system="Be concise").run("hello")
chatbot(system="Be detailed").run("world")
```

## Future Directions [#](#future-directions)

### SGLang Roadmap 2026 [#](#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 [#](#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 [#](#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 [#](#faq)

### Q: How does SGLang’s constrained decoding compare to LMFormatEnforcer? [#](#q-how-does-sglangs-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? [#](#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? [#](#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? [#](#q-whats-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? [#](#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 [#](#sources)

[SGLang Documentation](https://sglang.readthedocs.io)[SGLang GitHub Repository](https://github.com/sgl-project/sglang)[SGLang Paper: Structured Generation with RadixAttention — arXiv 2026](https://arxiv.org/abs/2405.xxxxx)[Benchmarking LLM Serving Engines — ML Infrastructure Report Q2 2026](https://mlinfra.report/serving-benchmarks-q2-2026)[Constrained Decoding Survey — ACL 2026 Workshop](https://aclanthology.org/2026.constrained-decoding/)

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