> Processing data at the edge also makes for the best possible user experience because of the complete independence of network connectivity and hence minimal la

> Processing data at the edge also makes for the best possible user experience because of the complete independence of network connectivity and hence minimal latency. I know a shop who's doing this and it's a very promising approach. The ability to offload the costs of cloud GPU time is a tremendous advantage. That's to say nothing of the decreased latency, increased privacy, etc. The glaring downside is that you are dependent upon your users to be willing and able to run native apps (or possibly WASM, I'm not sure) on bleeding edge hardware. However, for some target markets (e.g. video production, photography, designers, etc.) it's a "safe" assumption that they will be using the latest and greatest Macs. I've also been hearing people talk somewhat seriously about setting up their own training/inference farms using Macs because, at least for now, they're more readily available and cheaper to buy/run than big GPUs. That comes with a host of ops problems but it still may prove worthwhile for some use cases and addresses some of the same privacy concerns as edge computing if you're able to keep data/computation in-house.