A high-throughput and memory-efficient inference and serving engine for LLMs
Repository dossier
vllm-project/vllm-omni
A framework for efficient model inference with omni-modality models
Brief / README 摘要
查看原文Easy, fast, and cheap omni-modality model serving for everyone [2026/10] We released 0.32.0, aligned with vLLM 0.32.0, featuring optional Ray-based multi-node inference, session-aware full-duplex serving, server VAD for turn-based realtime requests, and expanded image, music, and speech generation with YuE2-3B, Qwen-Image-2.1, Kandinsky6, MOSS-Image, and TaoMate-H3. See the release notes for compatibility changes and installation instructions. [2026/10] We released the vLLM-Omni Technical Report, describing a unified serving runtime for text, audio, image, video, and action generation. [2026/0…
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