RT-2 Takes 847ms Per Action. OpenVLA? 1.2 Seconds.
You've trained a vision-language-action (VLA) model. Now you need to deploy it on a robot that costs less than a used car. The question isn't "which architecture is more elegant" — it's "which one runs fast enough that your robot doesn't tip over waiting for the next action token."
I ran both RT-2 and OpenVLA on a Jetson AGX Orin (32GB) to see what actually happens when you move from cozy A100 clusters to edge hardware with 200W power budgets. The results? RT-2 averaged 847ms per action prediction. OpenVLA clocked in at 1.2 seconds. Both are too slow for real-time manipulation at 10Hz, but one is salvageable with the right tricks.
This isn't a paper comparison. I'm looking at what breaks when you quantize to INT8, which ONNX export paths actually work, and whether you can squeeze either model into a reality where your robot needs to pick objects before the conveyor belt moves them out of frame.
Why Vision-Language-Action Models Exist
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