[cafed00d]

I like to think back to 2011 and paraphrase what people were saying: "Is anyone seriously using gpu hardware to write nl translation software at the moment?"

"No, we should be use cheap commodity abundantly available cpus and orchestrate then behind cloud magic to write our nl translation apps"

or maybe "no we should build purpose built high performance computing hardware to write our nl translation apps"

Or perhaps in the early 70s "is anyone seriously considering personal computer hardware to ...". "no, we should just buy IBM mainframes ..."

I don't know. Im probably super biased. I like the idea of all this training work breaking the shackles of cloud/mainframe/servers/off-end-user-device and migrating to run on peoples devices. It feels "democratic".

[seanmcdirmid]

I remember having lunch with a speech recognition researcher who was using GPUs to train DNNs to do speech recognition in 2011. It really was thought of as niche back then. But the writing was on the wall I guess in the results they were getting.

[diego_sandoval]

AMD didn't read the wall, unfortunately.

[fennecfoxy]

I don't think of examples really apply, because it's more a question of being on "cutting edge" vs personal hardware.

For example, running a local model and access to the features of a larger more capable/cloud model are two completely different features therefore there is no "no we should do x instead".

I'd imagine that a dumber local model runs and defers to cloud model when it needs to/if user has allowed it to go to cloud. Apple could not compete on "our models run locally privacy is a bankable feature" alone imo, TikTok install base has shown us enough that users prefer content/features over privacy, they'll definitely still need SoA cloud based models to compete.