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Easy, fast, and cheap omni-modality model serving for everyone
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Latest News 🔥
- [2026/03] We released 0.18.0 - strengthens the core runtime through a large entrypoint refactor and scheduler/runtime cleanups, expands unified quantization and diffusion execution, broadens multimodal model coverage, and improves production readiness across audio, omni, image, video, RL, and multi-platform deployments.
- [2026/03] Check out our first public project deepdive at the vLLM Hong Kong Meetup!
- [2026/03] vllm-omni-skills is a community-driven collection of AI assistant skills that help developers work with vLLM-Omni more effectively. These skills can be used with popular agentic AI coding assistants like Cursor IDE, Claude, Codex, and more.
- [2026/02] We released 0.16.0 - A major alignment + capability release that rebases onto upstream vLLM v0.16.0 and significantly expands performance, distributed execution, and production readiness across Qwen3-Omni / Qwen3-TTS, Bagel, MiMo-Audio, GLM-Image and the Diffusion (DiT) image/video stack—while also improving platform coverage (CUDA / ROCm / NPU / XPU), CI quality, and documentation.
- [2026/02] We released 0.14.0 - This is the first stable release of vLLM-Omni that expands Omni’s diffusion / image-video generation and audio / TTS stack, improves distributed execution and memory efficiency, and broadens platform/backend coverage (GPU/ROCm/NPU/XPU). It also brings meaningful upgrades to serving APIs, profiling & benchmarking, and overall stability. Please check our latest paper for architecture design and performance results.
- [2026/01] We released 0.12.0rc1 - a major RC milestone focused on maturing the diffusion stack, strengthening OpenAI-compatible serving, expanding omni-model coverage, and improving stability across platforms (GPU/NPU/ROCm).
- [2025/11] vLLM community officially released vllm-project/vllm-omni in order to support omni-modality models serving.