The study introduces an intelligence-per-watt efficiency metric.
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 excited by Stanford findings that local models now reach 71% accuracy on real-world tasks because the rapid gains signal major wins for privacy, cost, and practical viability over cloud options.
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
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!
There are many compelling reasons to use AI in the cloud. At the same time, local and offline models will get more and more powerful. This accelerates the democratization of AI and is another bulwark against centralization and monopolization.
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!
@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!
@ClementDelangue @Stanford Have a link to the study?
@DevaBuilds @Stanford yes! multi-model for the win!
The study introduces an intelligence-per-watt efficiency metric.
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!