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NVIDIA's Jetson is a line of edge AI compute modules/SoCs used for on-device inference in robotics, embedded systems, and smart devices.
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Lola Vision Systems will license its compiler on existing chips, aiming at what its founder says can be roughly 200 hours of manual setup per new chip. Cheaper porting would let edge teams test a second board, though the company has not said how many of those hours its software saves.
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SiMa.ai raised $150 million at a $1.45 billion valuation to scale its development environment and build its next embedded chip. For device makers on Nvidia's Jetson boards, the case turns on whether that software ports a model in the days or hours SiMa.ai claims.
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Where a robot's inference runs decides what the machine costs to build. SemiAnalysis puts the industry's upfront compute bill in the trillions at a billion robots, paid by manufacturers on every unit before it does any work.
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Nemotron 3.5 Lightning touches a tenth of its weights per token, which is what makes an agent loop plausible next to the sensors, but memory still has to hold all thirty billion and NVIDIA's own loop still escalates off-device.
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Two million Blackwell Ultra and Rubin parts, booked five months after a one million commitment. Custom silicon did not substitute for Nvidia; it added to the bill.
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The model teaches robot arms from hand demonstrations instead of per-task code. Generalist's own figures put completion at 83% with a few examples, which still leaves a person at the station.
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The Caper Cart's cameras, scale and edge compute turn a store trip into a measurable channel. The scale claims stay vague, but the ownership question does not.
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