Than it just makes sense that they already do this.
But to be fair, if i move, enough space for battery, solar and a basement which can take proper ML hardware + heat exchanger, is in my future vision.
Sooooo :D
The advantage is no more 5-hours or weekly session limit anymore. Also no chance your proprietary code ends up in someone repo.
That is not a concern with pay-as-you-go models like DeepSeek. It is stupid inexpensive.
I run local models, too, but every discussion about these models uses the local quantized versions and the hosted versions interchangeably. They’re not interchangeable. The quantized versions you can run on mid-grade consumer hardware are a step down in quality. That might be fine or might not depending on your use case but it needs to be acknowledged. It’s not until you get into really expensive hardware setups that you can run something like DSv4 Flash at Q8, and you’re not breaking even on that relative to a cloud provider.
Keeping everything local is about the only reason to run local. The other discussions about saving money are missing something.
For a lot of people, there are other variables though.
Your investment in hardware is of course fixed, the token cost amortized over time.
I suppose the other question is, do we think down the road that the models capable of being run locally will improve?
There are many overlapping/compounding curves all happening simultaneously pushing cost down, even on the same hardware - faster software, which includes improvements that massively improve memory/memory bandwidth efficiency for model (especially improved kvcache, specdec/MTP, expert loading becoming standard), improved architectures (no one using full attention, engrams), and a never-ending stream of training improvements.
I think one only has to look at Qwen 3.8 27B to see how top tier a smaller model (<15GB Q4) can be. IMO it's one of the first small models to be able to be run as an agentic coder (it's not quite Opus 4.6-level, but it's not that far off), and with MTP/DFlash2, it's runs surprisingly fast on consumer GPUs. The 30B-class MoEs run even faster, and with the right tuning/post-training I'm sure they can match frontier capabilities in many areas (including coding) over the next months. I think last year the open/local AI picture was a lot more grim than it feels like now, tbt.