The SGLang Production Handbook

Architecture, Deployment, and Optimization for Large Language Model Inference

Introduction: The Inference Stack and Why SGLang Exists

1. The inference serving problem
2. From research to production: why new serving stacks emerged
3. What SGLang is: frontend language and inference runtime
4. The SGLang design philosophy
5. Where SGLang fits in the ecosystem
6. How to read this book

Chapter 1: What is SGLang

1. The inference serving problem
2. From research to production: why new serving stacks emerged
3. What SGLang is: frontend language and inference runtime
4. The SGLang design philosophy
5. Where SGLang fits in the ecosystem
6. What to expect from the rest of this book

Chapter 2: Architecture Overview

1. High-level component diagram
2. The request lifecycle end-to-end
3. Frontend to runtime boundary
4. GPU execution model overview
5. Single-node vs. multi-node topology

Chapter 3: The SGLang Frontend Language

1. SGLang as a generation programming language
2. Select, generate, and control flow constructs
3. Structured generation with JSON and schema constraints
4. Tool calling and function calling syntax
5. Compiling SGLang programs to the runtime

Chapter 4: Installation and Environment Preparation

1. System requirements (OS, Python, CUDA/ROCm, GPU memory)
2. pip installation and quick start
3. Source installation and build options
4. Docker and containerized deployment
5. Accelerator environments (NVIDIA CUDA, AMD ROCm)
6. Model retrieval and Hugging Face integration
7. Dependency management and version compatibility
8. Common installation failures and fixes

Chapter 5: Launching and Configuring the Server

1. The launch command and its arguments
2. Model and tokenizer configuration
3. GPU selection and memory limits
4. Networking, ports, and host binding
5. Logging, debugging, and profiling flags
6. Production-ready launch examples

Chapter 6: Model Support and Compatibility

1. Supported model families and architectures
2. Transformer models and decoder-only serving
3. Multimodal and vision-language models
4. Mixture-of-experts and MoE-specific considerations
5. Long-context and specialized architectures
6. LoRA adapters and dynamic adapter loading
7. Tokenizer and chat-template gotchas
8. Bringing new models into SGLang

Chapter 7: The Scheduler and Request Lifecycle

1. Request ingestion and queuing
2. Prefill and decode phases
3. Continuous batching mechanics
4. Chunked prefill and memory pressure management
5. Scheduling policies and fairness
6. Timeout, eviction, and cancellation

Chapter 8: KV-Cache and RadixAttention

1. Why KV-caching matters for inference
2. Paged attention and memory pools
3. RadixAttention and prefix tree structure
4. How prefix caching reduces redundant computation
5. Cache eviction and replacement policies
6. Cache hit rate optimization strategies
7. Hierarchical and distributed caching

Chapter 9: Attention Backends and Kernels

1. The role of attention in inference performance
2. Available attention backends
3. Backend selection and hardware compatibility
4. CUDA kernels and kernel fusion
5. Diagnosing and optimizing backend performance
6. CUDA graphs and execution optimization

Chapter 10: Quantization

1. Why quantize: accuracy vs. memory vs. throughput
2. Supported formats (FP8, INT4, AWQ, GPTQ, etc.)
3. Model preparation for quantization
4. KV-cache quantization
5. Runtime configuration for quantized models
6. Validating quantized model quality
7. Hardware-specific quantization considerations

Chapter 11: Parallelism Strategies

1. Data parallelism for throughput
2. Tensor parallelism for large models
3. Pipeline parallelism and multi-stage execution
4. Expert parallelism for MoE models
5. Multi-LoRA batching and parallel adapter serving
6. GPU topology and interconnect requirements
7. NCCL and distributed communication
8. Process layout and node configuration

Chapter 12: Multi-Node and Cluster Deployment

1. Cluster topology and networking
2. Head node and worker node roles
3. Distributed tensor parallelism
4. Cross-node KV-cache considerations
5. Fault tolerance and node failure
6. Configuration and launch procedures

Chapter 13: Prefill-Decode Disaggregation

1. The prefill-decode split: motivation and theory
2. Disaggregated architecture components
3. Networking between prefill and decode
4. Load balancing and capacity allocation
5. When disaggregation helps and when it does not
6. Deployment patterns and practical configuration

Chapter 14: Speculative Decoding

1. How speculative decoding works
2. Draft model selection and configuration
3. Performance gains and overheads
4. When speculative decoding is worthwhile
5. Configuration and tuning

Chapter 15: APIs and Application Integration

1. OpenAI-compatible API endpoints
2. Streaming responses
3. Sampling parameters and controls
4. Tool/function calling endpoints
5. Multimodal request format
6. Authentication and request metadata
7. Error handling and client integration

Chapter 16: Structured Generation and Constrained Decoding

1. Grammar-based constrained generation
2. JSON and schema generation
3. Regex constraints
4. Deterministic vs. probabilistic structured outputs
5. Performance overhead of constrained decoding
6. Production patterns for structured outputs

Chapter 17: SGLang Model Gateway and Routing

1. What the Model Gateway is and why it exists
2. Worker registration and discovery
3. Routing strategies and load balancing
4. Health checks and failover
5. Multi-model deployment patterns
6. HTTP and gRPC paths
7. TLS, mTLS, and API-key protection
8. Kubernetes integration
9. Rate limiting and reliability
10. Observability

Chapter 18: Deployment Patterns

1. Developer laptop and single GPU
2. Dedicated inference server (multi-GPU)
3. Multi-node on-prem cluster
4. Kubernetes deployment
5. Cloud provider deployments
6. Production fleet patterns
7. Upgrade and rollback procedures

Chapter 19: Performance Engineering and Optimization

1. Key metrics: throughput, TTFT, ITL, GPU utilization
2. Benchmarking methodology and tools
3. Workload generation and realism
4. Identifying bottlenecks
5. Optimization as a decision process
6. Memory and KV-cache optimization
7. Batching and concurrency tuning
8. Compute and kernel optimization
9. Speculative decoding optimization
10. Network and distributed optimization
11. Optimization profiles: latency vs. throughput vs. long-context

Chapter 20: Capacity Planning and Production Sizing

1. GPU memory estimation (model, KV-cache, overhead)
2. Throughput and latency requirements
3. Concurrency and context-length impact
4. Replication and scaling calculations
5. Network bandwidth planning
6. Storage and model artifact planning
7. Headroom and failure capacity
8. Autoscaling considerations

Chapter 21: Observability and Operations

1. Logs and structured logging
2. Prometheus-compatible metrics
3. Metrics reference for operations
4. Tracing and request diagnostics
5. GPU monitoring and integration
6. Dashboards and alerting
7. Health and readiness checks
8. Profiling and incident diagnostics

Chapter 22: Security and Production Hardening

1. Network exposure and service isolation
2. TLS and mTLS configuration
3. API authentication and authorization
4. Secrets management
5. Container and Kubernetes security
6. Model and artifact integrity
7. Logging sensitive data
8. Multi-tenant isolation
9. Rate limiting and DoS protection
10. Prompt injection and safety

Chapter 23: Administration and Lifecycle Management

1. Process supervision and service management
2. Configuration management
3. Rolling deployments and upgrades
4. Model replacement and cache management
5. Log rotation and storage
6. Capacity expansion
7. Node replacement and migration
8. Disaster recovery considerations
9. Operational runbooks

Chapter 24: Troubleshooting and Failure Analysis

1. Installation and dependency failures
2. CUDA/ROCm and driver problems
3. Model loading and tokenizer errors
4. GPU out-of-memory conditions
5. KV-cache exhaustion
6. Initialization and startup failures
7. Distributed communication errors
8. Hangs, crashes, and degraded performance
9. Structured output and tool calling failures
10. Gateway and routing issues
11. Kubernetes-specific problems

Chapter 25: Configuration Reference

1. Server command-line arguments
2. Memory and cache parameters
3. Scheduling and batching parameters
4. Attention and performance parameters
5. Quantization parameters
6. Parallelism parameters
7. Distributed execution parameters
8. LoRA parameters
9. Speculative decoding parameters
10. MoE and expert parallelism parameters
11. PD disaggregation parameters
12. Observability and debugging parameters
13. Security parameters
14. Advanced and internal parameters
15. Environment variables
16. Model Gateway parameters

Conclusion: Building Resilient Inference Platforms

1. Key principles for production SGLang
2. Where SGLang is heading
3. Building resilient inference platforms
4. Final thoughts

References