Hugging Face's CEO argues local models will run most workloads.
Narrative violation: according to @Stanford research, local models can answer 71.3% of real-world chat and reasoning queries accurately, up from 23.2% in 2023. Obviously at a fraction of the cost and energy consumption of frontier APIs.
The obvious conclusion: you don't need a frontier model for most tasks. The future is multi-model: local, open-source, smaller and cheaper for the majority of workloads, frontier APIs when no other choices!
Many users are celebrating Stanford research showing local models reaching 71% accuracy on real-world tasks because it demonstrates rapid gains and makes local setups seem like the practical choice for privacy and control.
Epic research.
Not far off from personal experience : https://tomtunguz.com/using-local-ai-to-work-faster/
Narrative violation: according to @Stanford research, local models can answer 71.3% of real-world chat and reasoning queries accurately, up from 23.2% in 2023. Obviously at a fraction of the cost and energy consumption of frontier APIs.
The obvious conclusion: you don't need a frontier model for most tasks. The future is multi-model: local, open-source, smaller and cheaper for the majority of workloads, frontier APIs when no other choices!
Great paper from @JonSaadFalcon @Avanika15 @HazyResearch: https://huggingface.co/papers/2511.07885
@ClementDelangue @Stanford This is super cool Clem, 100% aligns with the model routing work we're doing.
@cooperawaken @Stanford this is the paper: https://huggingface.co/papers/2511.07885
@noah_vandal @Stanford the paper is here: https://huggingface.co/papers/2511.07885
@ClementDelangue @Stanford that 71% local accuracy jump is no joke, what stack are you running to skip the frontier api bill?
@QuinnyPig @Stanford here it is: https://huggingface.co/papers/2511.07885
@azeem I was just at Oslo Freedom Forum, so thinking a lot about AI for dissidents and activists right now. Local models matter a lot when the government can block / monitor / distort your cloud AI usage.
@ClementDelangue @Stanford 71.3% is the easy queries. That 28.7% failure rate is where frontier still earns its cost. Routing story, not a replacement story.
@ClementDelangue @Stanford This is going to radically accelerate once "more RAM" and "inference-tuned silicon" are standard on laptops.
@QuinnyPig @Stanford yes!
@ramez we are nearly getting there. tbh, i barely use my local models except for heartbeats. But I do know some people who do.
@ClementDelangue @Stanford the routing take is so sharp, that middle path is where most real world teams land what’s the wildest routing misstep you’ve spotted in production?
@ClementDelangue @Stanford Local AI will win 💪
@ClementDelangue @Stanford Interesting. do they say which size of local model? Is this a 4Gb type sized model or more like a 30Gb sized model (still technically 'local')
@DevaBuilds @ClementDelangue @Stanford Pareto every time, @TrismeGs !
@ClementDelangue @Stanford Thank you. I couldn't find it with some cursory searching; sorry to make you do my homework for me. Have a follow!
@ttunguz My internal plan is as follows
⬇️Details are as follows
@ClementDelangue @Stanford Have a link to the study?
@DevaBuilds @Stanford yes! multi-model for the win!
Hugging Face's CEO argues local models will run most workloads.
Narrative violation: according to @Stanford research, local models can answer 71.3% of real-world chat and reasoning queries accurately, up from 23.2% in 2023. Obviously at a fraction of the cost and energy consumption of frontier APIs.
The obvious conclusion: you don't need a frontier model for most tasks. The future is multi-model: local, open-source, smaller and cheaper for the majority of workloads, frontier APIs when no other choices!