Skip to content
inference-serving-sglang · claude-code-templates · Flowy
Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA,
One skill from claude-code-templates .
$ npx -y skills add davila7/claude-code-templates --skill inference-serving-sglang --agent claude-codeInstalls just this skill. Get the whole plugin for auto-invocation.
⚡ How it fires
How this skill gets triggered: by you, by Claude, or both.
Fires itself Claude auto-loads it when your prompt matches the work. You can call it Invoke it directly when you want it. Slash command /inference-serving-sglang👁️ Context preview
The summary Claude sees to decide when to auto-load this skill.
Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, 📦 Ships with claude-code-templates
</> SKILL.md
inference-serving-sglang.SKILL.md
---
name : sglang
description : Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.
version : 1.0.0
author : Orchestra Research
license : MIT
tags : [Inference Serving, SGLang, Structured Generation, RadixAttention, Prefix Caching, Constrained Decoding, Agents, JSON Output, Fast Inference, Production Scale]
dependencies : [sglang, torch, transformers]
---
# SGLang
High-performance serving framework for LLMs and VLMs with RadixAttention for automatic prefix caching.
## When to use SGLang
**Use SGLang when:**
- Need structured outputs (JSON, regex, grammar)
- Building agents with repeated prefixes (system prompts, tools)
- Agentic workflows with function calling
- Multi-turn conversations with shared context
- Need faster JSON decoding (3× vs standard)
**Use vLLM instead when:**
- Simple text generation without structure
- Don't need prefix caching
- Want mature, widely-tested production system
Flowy AI Flows that just work. Hand-picked Claude Code plugins that fire the right skill as you prompt.
**Use TensorRT-LLM instead when:**
- Maximum single-request latency (no batching needed)
- NVIDIA-only deployment
- Need FP8/INT4 quantization on H100
## Quick start
### Installation
```bash
# pip install (recommended)
pip install "sglang[all]"
# With FlashInfer (faster, CUDA 11.8/12.1)
pip install sglang[all] flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/
# From source
git clone https://github.com/sgl-project/sglang.git
cd sglang
pip install -e "python[all]"
```
### Launch server
```bash
# Basic server (Llama 3-8B)
python -m sglang.launch_server \
--model-path meta-llama/Meta-Llama-3-8B-Instruct \
--port 30000
# With RadixAttention (automatic prefix caching)
python -m sglang.launch_server \
--model-path meta-llama/Meta-Llama-3-8B-Instruct \
--port 30000 \
--enable-radix-cache # Default: enabled
# Multi-GPU (tensor parallelism)
python -m sglang.launch_server \
--model-path meta-llama/Meta-Llama-3-70B-Instruct \
--tp 4 \
--port 30000
```
### Basic inference
```python
import sglang as sgl
# Set backend
sgl.set_default_backend(sgl.OpenAI("http://localhost:30000/v1"))
# Simple generation
@sgl.function
def simple_gen(s, question):
s += "Q: " + question + "\n"
s += "A:" + sgl.gen("answer", max_tokens=100)
# Run
state = simple_gen.run(question="What is the capital of France?")
print(state["answer"])
# Output: "The capital of France is Paris."
```
### Structured JSON output
```python
import sglang as sgl
@sgl.function
def extract_person(s, text):
s += f"Extract person information from: {text}\n"
s += "Output JSON:\n"
# Constrained JSON generation
s += sgl.gen(
"json_output",
max_tokens=200,
regex=r'\{"name": "[^"]+", "age": \d+, "occupation": "[^"]+"\}'
)
# Run
state = extract_person.run(
text="John Smith is a 35-year-old software engineer."
)
print(state["json_output"])
# Output: {"name": "John Smith", "age": 35, "occupation": "software engineer"}
```
## RadixAttention (Key Innovation)
**What it does**: Automatically caches and reuses common prefixes across requests.
**Performance**:
- **5× faster** for agentic workloads with shared system prompts
- **10× faster** for few-shot prompting with repeated examples
- **Zero configuration** - works automatically
**How it works**:
1. Builds radix tree of all processed tokens
2. Automatically detects shared prefixes
3. Reuses KV cache for matching prefixes
4. Only computes new tokens
**Example** (Agent with system prompt):
```
Request 1: [SYSTEM_PROMPT] + "What's the weather?"
→ Computes full prompt (1000 tokens)
Request 2: [SAME_SYSTEM_PROMPT] + "Book a flight"
→ Reuses system prompt KV cache (998 tokens)
→ Only computes 2 new tokens
→ 5× faster!
```
## Structured generation patterns
### JSON with schema
```python
@sgl.function
def structured_extraction(s, article):
s += f"Article: {article}\n\n"
s += "Extract key information as JSON:\n"
# JSON schema constraint
schema = {
"type": "object",
"properties": {
"title": {"type": "string"},
"author": {"type": "string"},
"summary": {"type": "string"},
"sentiment": {"type": "string", "enum": ["positive", "negative", "neutral"]}
},
"required": ["title", "author", "summary", "sentiment"]
}
s += sgl.gen("info", max_tokens=300, json_schema=schema)
state = structured_extraction.run(article="...")
print(state["info"])
# Output: Valid JSON matching schema
```
### Regex-constrained generation
```python
@sgl.function
def extract_email(s, text):
s += f"Extract email from: {text}\n"
s += "Email: "
# Email regex pattern
s += sgl.gen(
"email",
max_tokens=50,
regex=r'[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}'
)
state = extract_email.run(text="Contact john.doe@example.com for details")
print(state["email"])
# Output: "john.doe@example.com"
```
### Grammar-based generation
```python
@sgl.function
def generate_code(s, description):
s += f"Generate Python code for: {description}\n"
s += "```python\n"
# EBNF grammar for Python
python_grammar = """
?start: function_def
function_def: "def" NAME "(" [parameters] "):" suite
parameters: parameter ("," parameter)*
parameter: NAME
suite: simple_stmt | NEWLINE INDENT stmt+ DEDENT
"""
s += sgl.gen("code", max_tokens=200, grammar=python_grammar)
s += "\n```"
```
## Agent workflows with function calling
```python
import sglang as sgl
# Define tools
tools = [
{
"name": "get_weather",
"description": "Get weather for a location",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string"}
}
}
},
{
"name": "book_flight",
"description": "Book a flight",
"parameters": {
"type": "object",
"properties": {
"from": {"type": "string"},
"to": {"type": "string"},
"date": {"type": "string"}
}
}
}
]
@sgl.function
def agent_workflow(s, user_query, tools):
# System prompt (cached with RadixAttention)
s += "You are a helpful assistant with access to tools.\n"
s += f"Available tools: {tools}\n\n"
# User query
s += f"User: {user_query}\n"
s += "Assistant: "
# Generate with function calling
s += sgl.gen(
"response",
max_tokens=200,
tools=tools, # SGLang handles tool call format
stop=["User:", "\n\n"]
)
# Multiple queries reuse system prompt
state1 = agent_workflow.run(
user_query="What's the weather in NYC?",
tools=tools
)
# First call: Computes full system prompt
state2 = agent_workflow.run(
user_query="Book a flight to LA",
tools=tools
)
# Second call: Reuses system prompt (5× faster)
```
## Performance benchmarks
### RadixAttention speedup
**Few-shot prompting** (10 examples in prompt):
- vLLM: 2.5 sec/request
- SGLang: **0.25 sec/request** (10× faster)
- Throughput: 4× higher
**Agent workflows** (1000-token system prompt):
- vLLM: 1.8 sec/request
- SGLang: **0.35 sec/request** (5× faster)
**JSON decoding**:
- Standard: 45 tok/s
- SGLang: **135 tok/s** (3× faster)
### Throughput (Llama 3-8B, A100)
| Workload | vLLM | SGLang | Speedup |
|----------|------|--------|---------|
| Simple generation | 2500 tok/s | 2800 tok/s | 1.12× |
| Few-shot (10 examples) | 500 tok/s | 5000 tok/s | 10× |
| Agent (tool calls) | 800 tok/s | 4000 tok/s | 5× |
| JSON output | 600 tok/s | 2400 tok/s | 4× |
## Multi-turn conversations
```python
@sgl.function
def multi_turn_chat(s, history, new_message):
# System prompt (always cached)
s += "You are a helpful AI assistant.\n\n"
# Conversation history (cached as it grows)
for msg in history:
s += f"{msg['role']}: {msg['content']}\n"
# New user message (only new part)
s += f"User: {new_message}\n"
s += "Assistant: "
s += sgl.gen("response", max_tokens=200)
# Turn 1
history = []
state = multi_turn_chat.run(history=history, new_message="Hi there!")
history.append({"role": "User", "content": "Hi there!"})
history.append({"role": "Assistant", "content": state["response"]})
# Turn 2 (reuses Turn 1 KV cache)
state = multi_turn_chat.run(history=history, new_message="What's 2+2?")
# Only computes new message (much faster!)
# Turn 3 (reuses Turn 1 + Turn 2 KV cache)
state = multi_turn_chat.run(history=history, new_message="Tell me a joke")
# Progressively faster as history grows
```
## Advanced features
### Speculative decoding
```bash
# Launch with draft model (2-3× faster)
python -m sglang.launch_server \
--model-path meta-llama/Meta-Llama-3-70B-Instruct \
--speculative-model meta-llama/Meta-Llama-3-8B-Instruct \
--speculative-num-steps 5
```
### Multi-modal (vision models)
```python
@sgl.function
def describe_image(s, image_path):
s += sgl.image(image_path)
s += "Describe this image in detail: "
s += sgl.gen("description", max_tokens=200)
state = describe_image.run(image_path="photo.jpg")
print(state["description"])
```
### Batching and parallel requests
```python
# Automatic batching (continuous batching)
states = sgl.run_batch(
[
simple_gen.bind(question="What is AI?"),
simple_gen.bind(question="What is ML?"),
simple_gen.bind(question="What is DL?"),
]
)
# All 3 processed in single batch (efficient)
```
## OpenAI-compatible API
```bash
# Start server with OpenAI API
python -m sglang.launch_server \
--model-path meta-llama/Meta-Llama-3-8B-Instruct \
--port 30000
# Use with OpenAI client
curl http://localhost:30000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "default",
"messages": [
{"role": "system", "content": "You are helpful"},
{"role": "user", "content": "Hello"}
],
"temperature": 0.7,
"max_tokens": 100
}'
# Works with OpenAI Python SDK
from openai import OpenAI
client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
response = client.chat.completions.create(
model="default",
messages=[{"role": "user", "content": "Hello"}]
)
```
## Supported models
**Text models**:
- Llama 2, Llama 3, Llama 3.1, Llama 3.2
- Mistral, Mixtral
- Qwen, Qwen2, QwQ
- DeepSeek-V2, DeepSeek-V3
- Gemma, Phi-3
**Vision models**:
- LLaVA, LLaVA-OneVision
- Phi-3-Vision
- Qwen2-VL
**100+ models** from HuggingFace
## Hardware support
**NVIDIA**: A100, H100, L4, T4 (CUDA 11.8+)
**AMD**: MI300, MI250 (ROCm 6.0+)
**Intel**: Xeon with GPU (coming soon)
**Apple**: M1/M2/M3 via MPS (experimental)
## References
- **[Structured Generation Guide](references/structured-generation.md)** - JSON schemas, regex, grammars, validation
- **[RadixAttention Deep Dive](references/radix-attention.md)** - How it works, optimization, benchmarks
- **[Production Deployment](references/deployment.md)** - Multi-GPU, monitoring, autoscaling
## Resources
- **GitHub**: https://github.com/sgl-project/sglang
- **Docs**: https://sgl-project.github.io/
- **Paper**: RadixAttention (arXiv:2312.07104)
- **Discord**: https://discord.gg/sglang
© 2026 Flowy · Free and open source
Built for Claude Code · Not affiliated with Anthropic
