AI/ML Inference Serving Framework
4.6k 2026-05-01
vllm-project/vllm-omni
A framework for efficient, fast, and cheap serving of omni-modality (text, image, video, audio) AI models.
Core Features
Omni-modality inference support (text, image, video, audio).
High-performance serving for both autoregressive and non-autoregressive models.
Flexible architecture with heterogeneous pipeline abstraction and distributed inference.
OpenAI-compatible API and seamless Hugging Face model integration.
Detailed Introduction
vLLM-Omni extends the renowned vLLM framework to support efficient inference and serving for omni-modality models, encompassing text, image, video, and audio data. It addresses the growing need for high-throughput, low-latency serving of complex AI models, including non-autoregressive architectures like Diffusion Transformers. By leveraging advanced KV cache management, pipelined execution, and flexible distributed inference capabilities, vLLM-Omni provides an easy-to-use, production-ready solution for deploying a wide range of multimodal AI applications, complete with an OpenAI-compatible API.