Principle:Sgl project Sglang Distributed Environment Setup
| Knowledge Sources | |
|---|---|
| Domains | Distributed_Computing, GPU_Parallelism |
| Last Updated | 2026-02-10 00:00 GMT |
Overview
A distributed initialization pattern that creates PyTorch process groups and configures inter-GPU communication for tensor-parallel and expert-parallel model serving.
Description
Distributed environment setup is the prerequisite for any multi-GPU model serving or quantization workflow. It initializes PyTorch's distributed backend (NCCL for GPU communication), assigns ranks and local ranks to processes, and creates world-level process groups. This enables tensor parallelism (splitting model layers across GPUs), pipeline parallelism (splitting model stages across GPUs), and expert parallelism (distributing MoE experts across GPUs). SGLang handles this automatically during Engine or Server initialization, but manual setup is required for standalone scripts (e.g., ModelOpt quantization).
Usage
Set up the distributed environment when running standalone multi-GPU scripts such as model quantization and export. For normal Engine or Server usage, this is handled automatically.
Theoretical Basis
Distributed initialization follows the SPMD (Single Program, Multiple Data) pattern:
- Each GPU process runs the same program with a unique rank
- NCCL (NVIDIA Collective Communication Library) enables efficient GPU-to-GPU communication
- Process groups define communication subsets for different parallelism strategies
Key concepts:
- world_size — Total number of participating processes
- rank — Unique global identifier for each process (0 to world_size-1)
- local_rank — GPU index on the local machine
- backend — Communication library (NCCL for GPU, Gloo for CPU)