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It feels like we're one technological breakthrough away from all of these data centers going up to be deemed irrelevant.
The promised mega-data center deals are meant to boost valuations today, not serve tons of customers three years from now.
My problem with LLMs (apart from philosophical aspects and economical impact) is that it would be unlikely for any of us to be able to train something functional locally (toy-like LLMs -- sure, but something really useful -- no). Apart from that it requires immense computing power, it also requires a dataset which is for the most part is obtained illegally.
I have to conclude that people would like to have powerful local AI but it should at the same time only be a tiny model. In which case it wouldn't be powerful.
Local models need to be resident in expensive RAM, the kind that has fat pipes to compute. And if you have a local app, how do you take a dependency on whatever random model is installed? Does it support your tool calling complexity? Does it have multimodal input? Does it support system messages in the middle of the conversation or not? Is it dumb enough to need reminders all the time?
Spend enough time building against local models and you'll see they're jagged in performance. You need to tune context size, trade off system message complexity with progressive disclosure. You simply can't rely on intelligence. A bunch of work goes into the harness.
Meanwhile, third party inference is getting the benefits of scale. You only need to rent a timeslice of memory and compute. It's consistent and everybody gets the same experience. And yes, it needs paying for, but the economics are just better.
My problem with LLMs (apart from philosophical aspects and economical impact) is that it would be unlikely for any of us to be able to train something functional locally (toy-like LLMs -- sure, but something really useful -- no). Apart from that it requires immense computing power, it also requires a dataset which is for the most part is obtained illegally.
I may personally be of modest intelligence, but to acquire the intelligence that I do have, I did not need to train on every book ever written, every Wikipedia article ever written, every blog post ever written, every reference manual ever written, every line of code ever written, and so on. In fact, I didn't train on even 1% of those materials, or even 0.00000000001% of those. The texts themselves were demonstrably not a prerequisite for intelligence.
At minimum, given that it only took me about 20 years of casual observation of my surroundings to approximate intelligence, this is proof positive that the only "dataset" you need is a bunch of sensors and the world around you.
And yes, of course, the human brain does not start from zero; it had a few million years of evolution to produce a fertile plot for intelligence to take root. But that fundamental architecture is fairly generic, and does not at all seem predicated on any sort of specific training set. You could feasibly evolve it artificially.
For the mainstream audience, the sentiment around local ai today is the same that they had around open source a few decades ago. For a few products, some paid solutions were so much more advanced that open source were very often completely overlooked. Why bother ? And the like. Then we had captive SaaS and other plateforms and now it's obviously wrong for most of us. The dependency we have with anthropic and openai f…
The huge difference to open source is that you can't just train an LLM with free time and motivation. You need lots of data and a lot of compute.
I sure want to be wrong on that, I definitely like the open-weight version of the future more
Local models are extraordinarily expensive if you're not maximizing throughput, and you're not going to be maximizing it. Local models need to be resident in expensive RAM, the kind that has fat pipes to compute. And if you have a local app, how do you take a dependency on whatever random model is installed? Does it support your tool calling complexity? Does it have multimodal input? Does it support system messages i…
Why not ship your own model? In the age of Electron apps, 10GB+ apps are not unheard of.
Earlier quoted context omitted.
What would that breakthrough be?
Magic math and computer science that allows us to get the same quality response for a fraction of the GPU.
If you project out that hardware just a couple of years, and the trained models out a couple of years, you end up in a place where it makes so much more sense to run them locally, for all sorts of latency, privacy, efficacy, and domain-specific reasons.
Not all that different from the old terminal & mainframe->pc shifts.
Finally - hardware has seemingly gotten out ahead of software that most folks use - watching YouTube, listening to music, playing a game or two. There was a time when playing an mp3 or watching a 4k video really taxed all but the nicest systems. Hardware fixed that problem, like it very well could this one.
Earlier quoted context omitted.
What would that breakthrough be?
Magic math and computer science that allows us to get the same quality response for a fraction of the GPU.
Basically small and medium models that are crazy well trained for their sizes.
Then we have a lot of specular decoding stuff like MTP and others coming to speed up responses, and finally better quantisation to use less memory.
Local LLM is the future, and the larger labs know that the open models will eat their lunch once people realise that the gap is only a few months. If we were good with LLMs a couple months ago, we're good with the open models now.