vla.cpp: A Unified Inference Runtime for Vision-Language-Action Models
Deploying vision--language--action (VLA) models on robots requires adapting model-specific inference pipelines to heterogeneous processors and limited onboard memory. We present vla.cpp, a unified C++ inference runtime for eleven VLA models, with no PyTorch dependency for model execution. The runtime shares model loading, tensor execution, and serving while retaining architecture-specific attention, conditioning, and action heads. Iterative policies reuse observation-dependent computation across solver steps, while regression policies predict actions directly. We evaluate task success on LIBERO-Object and profile supported configurations on NVIDIA, Apple, and Intel hardware. BitVLA comple...
| Authors | Khanh D. Nguyen, Hung T. Ho, Chinh T. Nguyen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 6 June 2026 |
| Last updated at source | 21 September 2026 |
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