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For sure many devices will have them, but the trick will be to build this local web model in a way that leverages all of the local chips. Apple’s advantage is i
by richardw 2y ago
For sure many devices will have them, but the trick will be to build this local web model in a way that leverages all of the local chips. Apple’s advantage is in not having to worry about all that. It has a simpler problem and better access to real local data.
Give my personal local data to a model running in the browser? Just feels a bit more risky.
- rfoo 2y ago> in a way that leverages all of the local chips Which, in a way, is similar to building a browser leveraging all of the local GPUs to do render and HW-accelerated video decoding. Is Safari on Apple Silicon better than Chrome on random Windows laptop for playing YouTube in the last 5 years? Hardly.
- cuu508 2y agoThe video plays fine on both, yes, but the Windows laptop, generally speaking, gets hotter, and runs out of battery sooner.
- ac29 2y agoIf you compare a random older Windows laptop to a new or nearly new Mac, sure. But new Windows/x86 PCs have gotten pretty efficient. Here's a review showing a Meteor Lake laptop getting 15 hours of video playback on battery: https://www.anandtech.com/show/21282/intel-core-ultra-7-115h-review-meteor-lake-makes-makes-fresh-start-to-mobile-cpus/10 https://www.anandtech.com/show/21282/intel-core-ultra-7-115h...
- rfoo 2y agoExactly. So feature-wise it can work, and out of our "I proudly spent $2500 on my fancy Apple laptop"-tech bubble, people already learned to settle on something hotter.
- supermatt 2y agoWhat $2500 laptops that people buy for watching YouTube are you referring to?
- richardw 2y agoAnd would still need to give Chrome access to your contacts, files etc to make it equivalent. It’s not useless, obviously it’ll be good, it’s just not the same. My original comment was replying to: “enable approximately what Apple is doing with their AI strategy”
- jitl 2y agoI think they’ll package a variety of model formats and download the appropriate one for the user’s system. Apple and Microsoft both offer OS-level APIs that abstract over the specific compute architecture so within the platform you don’t need further specialization. On Apple hardware they’ll run their model via CoreML on whichever set of compute (CPU, GPU, NPU) makes sense for the task. On Windows it will use DirectML for the same. I’m not sure if there’s a similar OS abstraction layer on Linux, possibly there they’ll just use CPU inference or whatever stack they usually use on Android.