DeepSeek launched a free, open-source large language model in late December, claiming it was developed in just two months at a cost of under $6 million.
That’s generally how tech goes though. You throw hardware at the problem until it works, and then you optimize it to run on laptops and eventually phones. Usually hardware improvements and software optimizations meet somewhere in the middle.
Look at photo and video editing, you used to need a workstation for that, and now you can get most of it on your phone. Surely AI is destined to follow the same path, with local models getting more and more robust until eventually the beefy cloud services are no longer required.
Or from the sounds of it, doing things more efficiently.
Fewer cycles required, less hardware required.
Maybe this was an inevitability, if you cut off access to the fast hardware, you create a natural advantage for more efficient systems.
That’s generally how tech goes though. You throw hardware at the problem until it works, and then you optimize it to run on laptops and eventually phones. Usually hardware improvements and software optimizations meet somewhere in the middle.
Look at photo and video editing, you used to need a workstation for that, and now you can get most of it on your phone. Surely AI is destined to follow the same path, with local models getting more and more robust until eventually the beefy cloud services are no longer required.