[Aligned with vllm 0.24.0 now][RFC]: vLLM-Omni 2026 Q2 Roadmap

Motivation.

This issue outlines the development roadmap for vllm-omni in Q2 2026. We are collecting your feedbacks and finalize it later.

Proposed Change.

1) Model Support

• Omni Models #2207
• TTS Models #2115
• Diffusion Models #2226 including world models and vla models

2) Feature Support

• Quantization #1854
• Prefix Cache #1184
• KV offload #1330
• Model Runner V2 #1770
• Streaming audio/video input/output #2208
• RL integration with veRL https://github.com/verl-project/verl/issues/5755
• Diffusion Optimizations #1217
• Diffusion continuous batching: #1769
• Diffusers backend #2403

3) Large Scale Deployment (Disaggregated Serving)

• EPDG disaggregation: x(E)y(P/D)zG #2336
• targeting models: Bagel/HY-Image3.0/Qwen-Omni/Ming-Omni

4) Hardware Support

• ROCm (AMD): #2413
• Intel XPU: #2574
• Ascend NPU: #2223
• MUSA: #2347

5) CI/CD & Usability

• Quality Gates: L1-L5 level tests enhancement&refactor #2299 #4197 #4254
• Benchmarking performance: vllm-omni-kanban
• Developer Usability: vllm-omni-skills
• User guidance: vllm/recipes

Feedback Period.

No response

CC List.

@ywang96 @Gaohan123 @youkaichao @Isotr0py @ZJY0516 @david6666666 @SamitHuang @linyueqian @wtomin @tzhouam @gcanlin @tjtanaa @xuechendi

Any Other Things.

No response

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