vllm - 💡(How to fix) Fix [Feature]: Adding VLA models support into vLLM

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🚀 The feature, motivation and pitch

Background

vLLM already provides a high-throughput inference and serving stack for LLMs and multimodal language models. However, Vision-Language-Action (VLA) models used in robotics and autonomous driving are not currently supported as a first-class model family in vLLM.

VLA models extend VLMs by taking multimodal observations such as images, language instructions, and robot/vehicle state, and returning actions or trajectories rather than only text tokens. Supporting this class of models in vLLM would enable:

  • high-throughput batched rollouts for robotics simulation, evaluation, and RL;
  • a unified serving backend for VLA models instead of one-off policy servers;
  • reuse of vLLM’s scheduler, request queueing, tensor/pipeline parallelism, metrics, and OpenAI-compatible serving infrastructure;
  • a clean extension point for future action-output models.

There is already implementation work in SGLang that can be used as a reference for scoping and parity testing:

Related SGLang tracking issue:

There is also a previous vLLM issue requesting VLA/OpenVLA support, but it was closed as not planned:

This issue proposes a scoped path to add first-class VLA inference support to vLLM, initially targeting four models that already have SGLang implementations.

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Additional context

Relevant SGLang work:

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vllm - 💡(How to fix) Fix [Feature]: Adding VLA models support into vLLM