Principle:Sgl project Sglang Server Arguments Configuration
| Knowledge Sources | |
|---|---|
| Domains | LLM_Serving, Configuration |
| Last Updated | 2026-02-10 00:00 GMT |
Overview
A configuration pattern that centralizes all inference server parameters into a single validated dataclass for consistent initialization across deployment modes.
Description
Server argument configuration is the practice of defining all tunable parameters for an LLM inference server — model path, parallelism, memory allocation, quantization, scheduling policy, logging — as a single typed dataclass. This ensures that whether the engine is launched programmatically or via CLI, the same validated configuration object drives initialization. SGLang's ServerArgs dataclass contains ~200 fields organized into groups: model/tokenizer, HTTP server, quantization/dtype, memory/scheduling, runtime options, logging, attention backends, parallelism, and more. The dataclass approach enforces type safety and default values, reducing misconfiguration errors.
Usage
Use server argument configuration when initializing any SGLang deployment — whether offline batch inference via Engine, online serving via launch_server, or distributed multi-GPU setups. This is always the first step before any model loading or serving begins.
Theoretical Basis
The pattern follows the Configuration Object design principle: a single immutable (after construction) object carries all settings through the system. This avoids scattered global state and makes configuration explicit and auditable.
Key design choices:
- Python dataclass with ~200 typed fields and sensible defaults
- CLI argument parser (add_cli_args) that mirrors the dataclass fields
- Factory method (from_cli_args) to construct from parsed CLI arguments
- Validation logic (prepare_server_args) that checks constraints and resolves "auto" values