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Comment by csomar | Hacker News Reader

I think because they are trained on Claude/O1, they tend to have comparable performance. The small models quickly fails on complex reasoning. The larger the models, the better the reasoning is. I wonder, however, if you can hit a sweet spot with 100gb of ram. That's enough for most professional to be able to run it on an M4 laptop and will be a death sentence for OpenAI and Anthropic.

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> I think because they are trained on Claude/O1, they tend to have comparable performance.

Why does having comparable performance indicate having been trained on a preexisting model's output?

I read a similar claim in relation to another model in the past, so I'm just curious how this works technically.

because the valley is burning money and GPUs training these and somebody else comes out with another model for a tiny fraction of cost it's an easy assumption to make it was trained on synthetic data

Do you have any evidence for this accusation?

O1's reasoning traces aren't even shown, are you suggesting they've somehow exfiltrated them?

At the price of $5,000 before taxes. There would be better and most cost effective options to run models that will require that much memory.

It is a laptop. The memory is also shared which means if you are looking for a non-gaming workload, you can use it. If you have laptop equivalents in the same memory range, feel free to share.

I have laptop equivalents in the same memory range and is at least $2,500 cheaper.

Unfortunately, it does not have "unified memory", a somewhat "powerful GPU", and of course no local LLM hype behind it.

Instead, I've decided to purchase a laptop with 128GB RAM with $2,500 and then another $2,160 for 10 years Claude subscription, so I can actually use my 128GB RAM at the same time as using a LLM.

Ok, but that means you’re not getting full privacy. It’s a trade off.

That's not the same thing. Also, can you share this 128GB $2500 laptop?

I see this comment all the time. But realistically if you want more than 1 token/s you’re going to need geforces, and that would cost quite a lot as well, for 100 GB.

https://nvidianews.nvidia.com/news/nvidia-puts-grace-blackwe...

GB10, or DIGITS, is $3,000 for 1 PFLOP (@4-bit) and 128GB unified memory. Storage configurable up to 4TB.

Can be paired to run 405B (4-bit), probably not very fast though (memory bandwidth is slower than a typical GPU's, and is the main bottleneck for LLM inference).

Not shipping until May or so.

That’s not something I can get, so it’s not really relevant. There is always a better device around the corner.

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