Principle:EvolvingLMMs Lab Lmms eval SGLang Acceleration
| Knowledge Sources | |
|---|---|
| Domains | Model Inference, Performance Optimization |
| Last Updated | 2026-02-14 00:00 GMT |
Overview
SGLang acceleration uses efficient inference backend for vision-language models with tensor parallelism and optimized memory management.
Description
SGLang (Structured Generation Language) is a high-performance inference backend that accelerates large vision-language models through tensor parallelism, optimized GPU memory utilization, and efficient visual processing. It supports multi-GPU setups, batched inference, and configurable resolution constraints for images and videos. The framework enables faster evaluation of models like Qwen3-VL while maintaining accuracy.
Usage
Apply this principle when evaluating large vision-language models (30B+ parameters) that require multi-GPU setups, when you need to optimize inference speed without sacrificing quality, or when processing mixed image and video content with varying resolutions.
Theoretical Basis
Key Concepts
- Tensor Parallelism: Distributes model weights across multiple GPUs to handle models larger than single GPU memory
- GPU Memory Utilization: Configurable memory fraction (0.0-1.0) balances between capacity and OOM risk
- Visual Processing Threads: Parallel threads decode and preprocess images/videos before model inference
- Resolution Constraints: Min/max pixel limits control memory usage and processing time per visual input
- Batch Processing: Groups multiple samples for efficient GPU utilization
Configuration Parameters
- tensor_parallel_size: Number of GPUs for model sharding
- gpu_memory_utilization: Fraction of GPU memory to use (e.g., 0.85 = 85%)
- max_pixels: Upper bound on image resolution (e.g., 1605632 ≈ 1267×1267)
- min_pixels: Lower bound on image resolution (e.g., 784 = 28×28)
- max_frame_num: Maximum frames extracted from videos
- threads: Parallel threads for visual decoding