Comment by JSR_FDED
Nobody is winning until cars are the size of a pack of cards. Which is big enough to transport even the largest cargo.
Nobody is winning until cars are the size of a pack of cards. Which is big enough to transport even the largest cargo.
You should start a company and try your strategy. I hope it works! (Though I am doubtful.)
In any case, models are useful, even when they don't hit these efficiency targets you are projecting. Just like cars are useful, even when they are bigger than a pack of cards.
If someone wants to fund me, Ill gladly work on this. There is no money in this though, because selling cloud service is much more profitable.
Its also not a matter of it working or not. It already works. Take a small model that fits on a GPU with a large context window, like Gemma 27b or smaller ones, give it a whole bunch of context on the topic, and ask it questions and it will generate very accurate results based on the context.
So instead of encoding everything into the model itself, you can just take training data, store it in vector DBs, and train a model to retrieve that data based on query, and then the rest of it is just training context extraction.
> There is no money in this though, because selling cloud service is much more profitable.
Oh, be more creative. One simple way to make money off your idea is:
(1) Get a hedge fund to finance your R&D.
(2) Hedge fund shorts AI cloud providers and other relevant companies.
(3) Your R&D pans out and the AI cloud providers' stock tanks.
(4) The hedge fund makes a profit.
Though I don't understand: wouldn't your idea work work when served from the cloud, too? If what you are saying is true, you'd provide a better service at lower cost?
Ok then point out where I made a mistake.
Nothing shows lack of understanding of the subject matter more than referencing the Dunning Kruger effect in a conversation.
Lol its kinda suprising that the level of understanding around LLMs is so little.
You already have agents, that can do a lot of "thinking", which is just generating guided context, then using that context to do tasks.
You already have Vector Databases that are used as context stores with information retrieval.
Fundamentally, you can have the same exact performance on a lot of task whether all the information exists in the model, or you use a smaller model with a bunch of context around it for guidance.
So instead of wasting energy and time encoding the knowledge information into the model, making the size large, you could have an "agent-first" model along with just files of vector databases, and the model can fit in a single graphics cards, take the question, decide which vector db it wants to load, and then essentially answer the question in the same way. At $50 per TB from SSD not only do you gain massive cost efficiency, but you also gain the ability to run a lot more inference cheaper, which can be used for refining things, background processing, and so on.