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Exactly, and all on an embedded system with quite restrictive settings and no overclocked Intel lastest generation combined with NVIDIA's 10k graphic cards.


Embedded systems can make network calls to powerful, GPU equipped servers.


Sure. Claude does that. "Cogitated for 1m 50s" doesn't work for real-time applications.


You can submit many queries in parallel to increase throughout. Smaller models and faster hardware can reduce the time per query too.


None of that gets you the 100ms response time the parent poster talked about, for something like "who is at my doorbell?" real-time uses.


Ok. Claude will not work for this use case because none of the sample data (weirdly blurry ID images) is in the training data.


They really shouldn't, though.


It can offer a ton of user value. There is a whole industry built upon this idea, Internet of Things.


IoT wasn't not built on "send all the data off to a hosted GenAI". It predated them by quite a few years.


The GPUs were doing video transcoding instead of GenAI.


You can run OpenCV on a GPU-less Raspberry Pi or other IoT device just fine.

And most IoT devices aren't doing video transcoding at all. You're making some very odd assertions in this thread.


>And most IoT devices aren't doing video transcoding at all.

The data gets streamed to the cloud where servers with GPUs transcode it. I'm pointing out that IoT devices historically have reached out to servers with GPUs even before GenAI.


Most IoT devices have no camera and communicate with servers that have no need for a GPU at all.




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