Basically SETI for AI workloads? At least, this is the impression the landingpage left me with.
I am a bit lost, tbh.
There is no distributed execution of a single LLM across multiple GPUs using tensor parallelism here. Instead, it relies on task-level parallelism-breaking down a large workload into independent subtasks that run across separate machines. For example, generating embeddings for a million documents.
Yes, we can distribute tasks to idle machines in homes and on office desktops, but it is often much more expensive than sending it to a data center where power is cheaper and the hardware is more efficient.
Then universities and business could reduce their need for cloud and have a reason to put that spending towards higher end Macs...
Maybe their chip team is already on the work, or already made, a server chip for AI purposes used internally, and doesn't have to talk about it yet.
Swift's main reason to go open source and have good Linux support was naturally for app developers to be able to use it on server side, which naturally wasn't going to be macOS.
I macOS wasn't required for developing iDevices apps, they would surely sell even less desktops.
Building the classification part now :) https://jeffyclassify.com/
https://arxiv.org/pdf/2610.12970
Shortest promoted paper I've seen from a FAANG.
I’m very curious if that project evolved to this……..going to message a few friends now heh
Apple has released some small models previously that seemed genuinely great, along with some quality research/efforts. I’m just not seeing the same level of depth here, am I missing something?