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Deploying VLA Models

Deploy trained Vision Language Models on robotic systems.

Deployment Targets​

  • Edge devices: Onboard compute (Jetson, mobile GPUs)
  • Cloud services: AWS SageMaker, Azure ML, Google Vertex AI
  • Hybrid: Edge inference with cloud training

Optimization Techniques​

  • Quantization: Reduce model size and latency
  • Pruning: Remove unnecessary parameters
  • Distillation: Transfer knowledge to smaller models
  • Caching: Pre-compute common operations

ROS 2 Integration​

Deploy VLA models as ROS 2 nodes for seamless integration.