Comment by MORPHOICES

I have been seeing these stories about bigger AI models that somehow work on small devices like a Raspberry Pi. One example really caught my eye lately. It was not just some quick show off thing. The model could interact and respond right away in real time. ~

That got me thinking again about what practical even means when it comes to AI running on the edge. Like, away from big servers.

I came up with a basic way to look at it. First, capability. That is, what kinds of tasks can it handle decently. Then latency. Does it respond quick enough so it does not feel laggy. There are also constraints to consider. Things like power use, how much memory it needs, and heat buildup.

The use case part seems key too. What happens if you try to take it off the cloud and run it locally. In my experience, a lot of these edge AI demos fall short there. The tech looks impressive, but it is hard to see why you really need it that way.

It seems like most people overlook that unclear need. I am curious about how others see it. Local inference probably beats cloud in spots where you cannot rely on internet, or maybe for privacy reasons. Or when data stays on device for security.

Some workloads feel close right now. They might shift soon as hardware gets better. I think stuff like voice assistants or simple image recognition could tip over.

If someone has actually put a model on limited hardware, like in a product, what stood out as a surprise. The thermals maybe, or unexpected power drains. It feels like that part gets messy in practice. I might be oversimplifying how tricky it all is.