vLLM-Omni: Serving Qwen3-Omni with a Multi-Stage Pipeline for Multimodal Speech Generation
vLLM-Omni introduces a multi-stage serving pipeline for Qwen3-Omni, a multimodal model combining text, image, and audio understanding with speech generation. The system breaks inference into three stages: Thinker (multimodal reasoning), Talker (speech codec generation), and Code2Wav (waveform reconstruction). The post details optimizations including stage-level batching, CUDA Graphs, async chunk processing, async output streaming, per-stage replicas, and hot-path cleanup to achieve low-latency online serving. It also covers OpenAI-compatible API endpoints, performance validation results, and lessons learned from deploying this architecture in production.
Key quotes
vLLM-Omni's Qwen3-Omni serving stack includes: A three-stage pipeline: Thinker for multimodal reasoning, Talker for speech codec generation, and Code2Wav for waveform reconstruction.
OpenAI-compatible serving: /v1/chat/completions is the primary endpoint for Qwen3-Omni text and audio generation.
Batching, CUDA Graphs, async chunk, async output, replicas, and hot-path cleanup: stage-level batching and per-stage
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