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PyTorch

model1 events
papersTODAY 04:00 UTC

Study Compares SmolVLA Task Success and Latency Across PyTorch and ONNX Deployments

A new arXiv paper examines how deploying the SmolVLA vision-language-action model in different runtime formats affects both inference speed and closed-loop task performance. The authors benchmark HuggingFaceVLA/smolvla_libero on a 6 GB RTX 2060 across the LIBERO Spatial and Object suites using MuJoCo and LeRobot with a fixed seed. The results indicate that cutting latency through optimized deployment can shift task behavior, so faster inference does not automatically mean better outcomes.

vision-language-action/benchmarks/inference-latency/robotics