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▲ sdoering 7 hours ago

Pardon me not understanding what this is about?

Basically SETI for AI workloads? At least, this is the impression the landingpage left me with.

I am a bit lost, tbh.

▲railka 7 hours ago | parent [-]

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.

▲dekhn 3 hours ago | parent | next [-]

This pattern has been implemented across hundreds if not thousands of different organizations (and between organizations). I have a patent on data distribution strategies from my time at Google running a large (>1M cores 24/7 for 3 years) idle cycle harvesting project.

it's a good idea but you often end up with huge tail latencies, slight incompatibilities due to fleet heterogeneity, and a database/scheduler management problem.

▲greggsy 6 hours ago | parent | prev | next [-]

My university ran its render farm across idle workstations using the screensaver back in early 2000. Same concept.

▲ 5 hours ago | parent | next [-]
[deleted]
▲juliendorra 4 hours ago | parent | prev | next [-]

[dead]

▲pjmlp 4 hours ago | parent | prev [-]

Psst, it is "innovative". /s

▲gizajob 4 hours ago | parent [-]

It is if it’s GPU compute.

▲pjmlp 3 hours ago | parent [-]

Distributed GPU compute has existed for quite some time,

https://developer.nvidia.com/gpudirect

And this is because I can't be bothered to dive into older papers, so recent NVIDIA stuff will do.

▲alt227 7 hours ago | parent | prev [-]

So basically works the same way as Folding@Home?

▲rafaelmn 7 hours ago | parent | next [-]

Embedding@Work

▲cr125rider 6 hours ago | parent [-]

HotBackpack@Commute

▲1e1a 6 hours ago | parent [-]

DeadBattery@Work

▲rramon 7 hours ago | parent | prev | next [-]

Only on the same local network I guess.

▲coob 6 hours ago | parent | prev [-]

or distributed.net