Minions is an internal enterprise system already in use at Apple. It pools idle Mac GPUs into a shared compute cluster for ML inference.
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.
I guess for companies this can be nice? Run some inference in the off-hours on the machines that you already have. Could be useful for analytical tasks that don't require to be instant. On the technical side this doesn't seem that groundbreaking for me though.
My warm take is that Apple is leaving a lot of money on the table by not investing in creating an independent supply chain for their chips and building their own cloud. They arguably had the first mover advantage when it came to general purpose arm compute; all they had to do was flood the market and make it easy for anyone to rent it. Not only would they corner a part of the market but perhaps ease demand pressure on other chip manufacturers too.
Apart from straight up buying TSMC's or Samsung's fabs I don't see a way of building a vertically integrated high-end chip supply chain for Apple. At least not on timeframes measured below a decade. And on those time frames the current supply issues will probably be resolved by companies who put all their effort into it instead of just tweaking their bottom line.
My guess is that they have challenges scaling the production anyway (6-16 weeks wait time for Studios), and also they would leave money in the table if they just released chips. I would assume that once they scale the production they will get back to the server game - MacOS server racks, or something more inventive like MacOS supercomputers running on a ton of prev gen chips ;)
They don't get enterprise, A/UX, mkLinux, OS X Server, Mac Pro tower, all gone now.
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.
It would be so incredibly cool if we finally had an accessible consumer-accessible way to turn a network of computers on a network into a supercomputer. A household has a child who gets into 3D artwork and wants to render something? People can opt their tablets, phones, laptops, whatever to crunch out some frames overnight while not being used.
until you get a teen obsessed with 3d rendering then they're up all night finding a way to convert manga into 3d scenes and your old laptop is running 100% cpu trying to keep up. xDD
The idealized version of this in my mind has a consent-first interface which makes it easy to set boundaries and limits and wouldn't burn out the machine as a default
Would love to build something like this but for classification models that run on CPU. Like a “Classify @ Home” p2p compute network, to which people could contribute their machines and see what work is being done
That seems to be the point: you leverage existing workstations on your corporate network and run compute batches at night while they're idle. You don't have to buy any extra servers for your low-priority AI workloads.
We were doing this decades ago with engineer's HP C3600s under their desks. It wasn't AI, just low-priority HPC / fluid stuff batched out to hundreds of workstations.
That was the original pitch for Ian Murdock's Progeny startup in the late 90s: a Linux-based distributed filesystem for corporate desktops to boot into overnight for distributed computing.
I don't get it. In the paper it sounds like it's "just" a CRUD DB, then you have some fleet of Macs, and they store inputs and outputs in S3. What's the innovation here? Not trying to poo on this, but it looked like the most basic and obvious approach.
It's not a novel idea, but its provable that someone can take this same concept and do something truly groundbreaking. I wonder if there's value in the idea that you can join a supercomputer network of MAC/Nvidia GPUs as a way of getting off cloud and into "our cloud". As long as you offer compute, you have credit into the system to be able to support your own workloads that can't be done on just your hardware. Need to address concerns about privacy, abuse, etc, but Apple's idea here has some merit if applied right.
Reading the “paper”, this is just a job runner built on Ollama.
Exo, and the teams at Apple that built the foundations Exo uses, actually use modern distributed compute.
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?
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.
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.
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?
What is it actually like?
nothing new here.