How AI Models Got Faster Without Losing Their Smarts
Researchers found a way to make large AI models run much faster while keeping them just as accurate.
By changing how a model is built inside — not just making it bigger — they cut the cost of running it, so apps can respond quicker and cheaper.
The team trained over 200 versions of models and discovered a simple, new rule that predicts which designs work best for a given amount of training.
The result: models that are up to 42% more efficient at answering questions and about 2% more accurate than common open models, using the same training time.
This means the phones, sites, or bots that use these models can be both smarter and kinder to budgets.
It also shows small design choices can have big effects on speed and cost.
The work points a clear path to making future AI that’s powerful, less expensive to run, and ready for everyday use — even on devices that dont have huge compute power.
Read article comprehensive review in Paperium.net:
Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs
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