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Open ecosystem drives inference/training cost down — Intel — VibeLeaderboard

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Open ecosystem drives inference/training cost down

From Local Models: Trust, Control, Optimization — Carter Abdallah, NVIDIA · ≈26:15

“So, I think in general, like the open models are only getting through the open ecosystem like more and more efficient like and and cheaper and cheaper to run and train on.”

“Like they might drive down the optimization, but then might not pass through those savings.”

What’s in it

Explains how open-model ecosystems slash inference and training costs together
Names Nvidia and ViLM as key partners squeezing efficiency out of models
Argues closed API providers pocket savings instead of passing them to users

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Clip transcript

frontier. Um and I think the other piece is like optimization, where like I think um like GM is a great example or like also like Trinity and NeMo Triton is like you have the whole ecosystem sort of like driving down the cost and optimizing it further, right? Like like we very heavily work like very closely with all the teams here, but then also um like deeply also with Nvidia and with teams like ViLM to really drive down uh the cost and and make the for example inference and training for models like GM or like models like Trinity and NeMo Triton extremely efficient, so you can basically drive down the cost like further and further. And I think this is something you don't obviously get with the closed APIs, where like they have like a huge margin on top. Like they might drive down the optimization, but then might not pass through those savings. So, I think in general, like the open models are only getting through the open ecosystem like more and more efficient like and and cheaper and cheaper to run and train on. So, I think there's like this element as well.