alt Hacker NewsNice, I think this is the second time I see this here on HN, I always wondered why we need to shove the entire model into memory, I don't care who King Charles is every single time. It always felt as though we already figured out how to break up large files and parse them efficiently with very little memory.
Frontier AI feels like its full of people who are brilliant at making models, but when it comes to scale and practicality, they just leave it to whoever sets up infrastructure to worry about. I wouldn't be surprised if frontier AI could be drastically cheaper if they just finetune and optimize their models to not consume all available RAM to only access less than 10% of the models knowledge.
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I don't care who King Charles is every single time
that's the trick and a multi-billion dollar question, how would an llm engine know that? it's an active research area how to cull the initial layer surface and do the optimal traversal path through the layers and it's a damn hard problem. It's definitely an area where a ton of performance is left on the table still.
> It always felt as though we already figured out how to break up large files and parse them efficiently with very little memory.
The A in 26B-A4B is the active weights.
The problem is that this is a per-token load/unload at best, not for the whole prompt.
The division happened until one of these can fit in a single GPU and they stopped scaling it down any more, because you can wire up 8 of them to do their share of the work.
> I don't care who King Charles is every single time
Always curious when someone will figure out how we can elide most of the data from an LLM (but retain the logical ability). I don't actually need an LLM to have a very big internal knowledge base to be useful, so long as it can invoke a search tool...
You're right, but the "just" in "just finetune" is doing _a lot_ of work here.
It's still early days and we "just" don't really know how to do it well.
Putting the whole model in memory is far faster then swapping to disk.
You're kinda describing the MoE architecture; you can offload expert layers and stream them as-needed if the experts are small enough and the SSD is fast enough.
Dense LLMs typically perform better, but slow down much more than MoE models when you try offloading layers.
> I always wondered why we need to shove the entire model into memory, I don't care who King Charles is every single time.
That's kind of the problem, isn't it? How do you know which part of the model to put in memory? You have to make a per-parameter decision of whether or not it's worth it to have it in memory or whether the value should just be treated as zero. Then you have to "re-link" the layers of the model to the new positions of each of the weights. For billions of parameters, that's a lot of calculations. And it requires us to know what each parameter actually represents, which nobody does.