Vllm Omni Readme Md At Main Vllm Project Vllm Omni Github

There was an error while loading. Please reload this page. We are excited to announce the official release of vLLM-Omni, a major extension of the vLLM ecosystem designed to support the next generation of AI: omni-modality models. Since its inception, vLLM has focused on high-throughput, memory-efficient serving for Large Language Models (LLMs). However, the landscape of generative AI is shifting rapidly. Models are no longer just about text-in, text-out.

Today’s state-of-the-art models reason across text, images, audio, and video, and they generate heterogeneous outputs using diverse architectures. vLLM-Omni is one of the first open source frameworks to support omni-modality model serving that extends vLLM’s exceptional performance to the world of multi-modal and non-autoregressive inference. Traditional serving engines were optimized for text-based Autoregressive (AR) tasks. As models evolve into “omni” agents—capable of seeing, hearing, and speaking—the serving infrastructure must evolve with them. vLLM-Omni addresses three critical shifts in model architecture: This page provides a comprehensive guide for deploying Qwen2.5-Omni using vLLM, a high-performance inference engine specifically optimized for large language models.

vLLM offers significant improvements in throughput, latency, and memory efficiency compared to standard deployment methods, making it ideal for production environments. For standard Transformers-based deployment, see Basic Usage Guide. For Docker container deployment, see Docker Deployment. For API integration, see API Integration. vLLM support for Qwen2.5-Omni requires a custom fork with special implementation for this model: vLLM's implementation for Qwen2.5-Omni is designed to work with the model's Thinker-Talker architecture, providing both text-only and audio output capabilities.

vLLM offers two primary deployment modes for Qwen2.5-Omni: | Documentation | User Forum | Developer Slack | vLLM was originally designed to support large language models for text-based autoregressive generation tasks. vLLM-Omni is a framework that extends its support for omni-modality model inference and serving: vLLM-Omni is flexible and easy to use with: vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:

We welcome and value any contributions and collaborations. Please check out Contributing to vLLM-Omni for how to get involved. Easy, fast, and cheap LLM serving for everyone vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. Where to get started with vLLM depends on the type of user.

If you are looking to: For information about the development of vLLM, see: There was an error while loading. Please reload this page. There was an error while loading. Please reload this page.

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Documentation: https://github.com/QwenLM/Qwen2.5-Omni | Documentation | Blog | Paper | Twitter/X | User Forum | Developer Slack | [2025/04] We're hosting our first-ever vLLM Asia Developer Day in Singapore on April 3rd! This is a full-day event (9 AM - 9 PM SGT) in partnership with SGInnovate, AMD, and Embedded LLM. Meet the vLLM team and learn about LLM inference for RL, MI300X, and more! Register Now

vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. This document provides a high-level introduction to vLLM, a high-performance inference and serving engine for Large Language Models (LLMs). It covers vLLM's purpose, key innovations, architectural layers, and the major code components that implement the system. For installation instructions, see Installation and Setup. For detailed information about specific subsystems, refer to the numbered sections in the wiki.

vLLM is a library for fast and memory-efficient LLM inference and serving. Originally developed at UC Berkeley's Sky Computing Lab, it is now a community-driven project hosted under the PyTorch Foundation. The project is designed to maximize throughput and minimize latency for LLM workloads across diverse hardware platforms. PagedAttention is vLLM's core innovation for memory management. It organizes KV cache into fixed-size blocks stored in non-contiguous memory, similar to virtual memory in operating systems. This enables:

The implementation is found in vllm/attention/backends/ with block management in vllm/core/block_manager.py Unlike static batching, vLLM continuously processes incoming requests as they arrive and completes. The Scheduler class vllm/core/scheduler.py decides which requests to run at each iteration, maximizing GPU utilization. Easy, fast, and cheap LLM serving for everyone vLLM is a fast and easy-to-use library for LLM inference and serving. Efficient management of attention key and value memory with PagedAttention

Continuous batching of incoming requests Fast model execution with CUDA/HIP graph

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