Principle:Sgl project Sglang Vision Language Inference
| Knowledge Sources | |
|---|---|
| Domains | Vision, Multimodal, Inference |
| Last Updated | 2026-02-10 00:00 GMT |
Overview
An inference pattern that processes combined visual and textual inputs through a vision-language model to produce text descriptions, answers, or analyses.
Description
Vision-language inference combines visual understanding with text generation. The model processes images/videos through a visual encoder, aligns the visual features with the text embedding space, and generates text responses conditioned on both modalities. SGLang supports this through the same Engine.generate method used for text-only inference, with the addition of image_data, video_data, and audio_data parameters. The multimodal processor is auto-detected from the model configuration.
Usage
Use vision-language inference for image captioning, visual question answering, image-based reasoning, document understanding, video summarization, and any task that requires understanding visual content and producing text output.
Theoretical Basis
VLM architecture follows an encoder-decoder-with-projection pattern:
- Visual Encoder (e.g., ViT, SigLIP): Converts images to feature vectors
- Projection Layer: Aligns visual features to text embedding space
- Language Model: Processes interleaved text and visual embeddings
- Text Generation: Produces output tokens auto-regressively
The visual features are inserted at image token positions in the combined input sequence, allowing the model to attend to both visual and textual context.