SGLang — Structured Generation and Fast LLM Serving Engine

SGLang, an open-source LLM inference engine, introduces RadixAttention for prefix caching and grammar-constrained decoding to achieve 25x throughput improvement over vLLM for structured output tasks. The engine supports models from 1B to 70B parameters on single or multi-GPU setups, enabling reliable JSON schema enforcement and complex reasoning patterns like ReAct and tool calling.

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/ Join our Telegram Group for real-time AI tool discussions and deployment tips: t.me/dibi8