Vllm Omni A Framework For High Performance Cost Efficient Inference

| 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. 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: A framework for high-performance, cost-efficient inference and serving of omni-modality models across text, image, video, and audio. vLLM-Omni is a framework designed for inference and serving of omni-modality models, supporting text, image, video, and audio inputs as well as heterogeneous outputs.

Built on vLLM’s efficient inference foundations, vLLM-Omni extends support to non-autoregressive architectures (e.g., Diffusion Transformers) and parallel generation models, enabling production-grade deployment with improved throughput and cost efficiency. High-throughput, memory-efficient inference and serving engine for large … A Next.js web application that integrates AI into draw.io to support … An open-source streaming dialogue text-to-speech (TTS) model and inference … Core Question Addressed: How can we efficiently serve the next generation of AI models that process and generate text, images, audio, and video, overcoming the limitations of serving engines designed only for text-based Autoregressive... The landscape of generative AI is undergoing a profound transformation.

Models are rapidly evolving from specialized Large Language Models (LLMs) to powerful “omni-agents” capable of seamlessly reasoning across and generating content in text, images, audio, and video modalities. This shift—from “text-in, text-out” to complex, heterogeneous input and output—demands an equally revolutionary shift in the underlying infrastructure. Serving these cutting-edge omni-modality models presents unique challenges that traditional serving engines, highly optimized for text-based Autoregressive (AR) tasks, cannot adequately handle. We are excited to introduce vLLM-Omni, a major extension of the renowned vLLM ecosystem. It stands as one of the first open-source frameworks specifically engineered to extend vLLM’s exceptional performance—namely, its high-throughput and memory-efficient serving—to the entire world of multi-modal and non-autoregressive inference. vLLM-Omni is designed to make omni-modality model serving accessible, efficient, and cost-effective for everyone.

vLLM is an open-source, high-throughput inference engine designed specifically for serving large language models (LLMs) at scale. Created by UC Berkeley researchers and supported by AnyScale, its mission is simple: make LLM deployment faster, cheaper, and more memory-efficient—especially in GPU-based environments where performance bottlenecks often stem from memory fragmentation and concurrency... By prioritizing efficient memory usage and real-time responsiveness, vLLM enables developers to serve instruction-tuned models like LLaMA, Vicuna, and Mixtral with subsecond latency—even under heavy, multi-user load. It stands apart for its ability to maintain high throughput and low latency simultaneously, a feat rarely achieved by general-purpose inference frameworks. Under the hood, vLLM introduces innovations like PagedAttention and continuous batching that give it a technical edge. These aren’t just buzzwords—they’re the mechanisms that let it serve tens of thousands of requests per day without wasting GPU memory or sacrificing response times.

Think of PagedAttention as a smarter way to handle memory, breaking up attention cache into small, swappable blocks. And continuous batching? That’s how vLLM avoids the pause-and-wait cycle common in static batching systems, dynamically inserting new sequences mid-generation without losing speed or efficiency. We’ll unpack both of these—along with streaming output, quantization support, and distributed inference—in detail later. But for now, just know this: vLLM isn’t just another backend. It’s a purpose-built system for turning high-performance LLM serving into something scalable, cost-effective, and production-ready.

<img alt="vLLM" src="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/logos/vllm-logo-text-light.png" width=55%> | Documentation | Blog | Paper | Discord | Twitter/X | Developer Slack | vLLM x Snowflake Meetup (Wednesday, November 13th, 5:30-8PM PT) at Snowflake HQ, San Mateo We are excited to announce the last in-person vLLM meetup of the year! Join the vLLM developers and engineers from Snowflake AI Research to chat about the latest LLM inference optimizations and your 2025 vLLM wishlist! Register here and be a part of the event!

vLLM is a fast and easy-to-use library for LLM inference and serving. Have you ever wondered how AI-powered applications like chatbots, code assistants and more respond so quickly? Or perhaps you’ve experienced the frustration of waiting for a large language model (LLM) to generate a response, wondering what’s taking so long. Well, behind the scenes, there’s an open source project aimed at making inference, or responses from models, more efficient. vLLM, originally developed at UC Berkeley, is specifically designed to address the speed and memory challenges that come with running large AI models. It supports quantization, tool calling and a smorgasbord of popular LLM architectures (Llama, Mistral, Granite, DeepSeek—you name it).

Let’s learn the innovations behind the project, why over 40k developers have starred the project on GitHub and how to get started with vLLM today! As detailed in our vLLM introductory article, serving an LLM requires an incredible amount of calculations to be performed to generate each word of their response. This is unlike other traditional workloads, and can be often expensive, slow and memory intensive. For those wanting to run LLMs in production, this includes challenges such as: With the need for LLM serving to be affordable and efficient, vLLM arose from a research paper called, “Efficient Memory Management for Large Language Model Serving with Paged Attention," from September of 2023, which... The results?

Up to 24x throughput improvements compared to similar systems such as HuggingFace Transformers and Text Generation Inference (TGI), with much less KV cache waste. Let’s briefly touch on the techniques used by vLLM in order to improve performance and efficiently utilize GPU resources: Serve high throughput inference with vLLM Evaluate accuracy with LM Evaluation Harness vLLM is an open-source, high-throughput inference and serving engine for large language models (LLMs). It’s designed to make LLM inference faster, more memory-efficient, and scalable, particularly during the prefill (context processing) and decode (token generation) phases of inference.

vLLM supports Hugging Face Transformer models out-of-the-box and scales seamlessly from single-prompt testing to production batch inference. In this Learning Path, you’ll build a CPU-optimized version of vLLM targeting the Arm64 architecture, integrated with oneDNN and the Arm Compute Library (ACL). This build enables high-performance LLM inference on Arm servers, leveraging specialized Arm math libraries and kernel optimizations. After compiling, you’ll validate your build by running a local chat example to confirm functionality and measure baseline inference speed. vLLM (Virtual Large Language Model) is an open-source library designed to optimize the inference and serving of large language models (LLMs). Originating from the Sky Computing Lab at UC Berkeley, vLLM addresses the challenges of deploying LLMs in production, such as high memory consumption, latency, and computational demands.

By leveraging innovative techniques like PagedAttention and continuous batching, vLLM achieves up to 24x higher throughput and significantly reduces memory usage compared to traditional LLM serving systems. vLLM is particularly valuable for organizations aiming to deploy LLMs efficiently, whether for real-time applications like chatbots or batch processing for content generation. Its compatibility with popular models (e.g., Llama, Mistral, Granite) and integration with frameworks like Hugging Face, LangChain, and Kubernetes make it a versatile choice for developers. This blog explores vLLM’s architecture, use cases, implementation in an Azure Kubernetes Service (AKS) cluster, industry applications, best practices, limitations, and a conclusion. vLLM’s architecture is designed to maximize performance, scalability, and resource efficiency. It consists of several key components that work together to streamline LLM inference and serving.

Below is an overview of its architecture and building blocks. vLLM operates as a high-performance inference engine with a modular, layered architecture: 1. Input Layer: Handles incoming requests via an OpenAI-compatible API server, supporting chat completions and model queries.

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