laptop is either a macbook or thinkpad for me, i'd buy for portability and use cloud resources/ university servers for actual computation

if not the cloud because u don't want ongoing payment than remote compute on a desktop with 256 ram&4090 heating the office, an instance like that is a beast for ML compared to these...slim contenders, university professors usually do that, they buy a heavy lifter for a few of them to use freely from their mb airs or thinkpads

Yeah I brought a used gaming desktop from Facebook and kept my 6 year old laptop. Crazy specs for the price and came with a 3060 with 12gb vram. I recommend a gpu with more vram vs one that's "faster" because it won't be fast if it can't load the model at all

You could probably look at the 11th gen legion 7i which is cheaper than their new 12th gen ones. They're not 3080TI but the difference between 3080 and 3080 ti, last I check was very minimal like 5% performance difference.

I personally have the 11th gen version after comparing a bunch of gaming laptops and use it for programming in Unreal Engine and deep learning and playing with Stable Diffusion, etc. Main pro? Like you said, the looks. I love the simple minimal non gaming laptop appeal of the legions. 😅

Also, you'd probably wanna research if all the laptops you've listed are actually able to run the 3080s at their max rating of 150w (previously known as max-q i believe). Some oems won't advertise it. The legion 7i 3080s are though.

How's the performance in deep learning and data science in general

I'm fine with 12th gen too. Cost not an issue. Just want an overall elite product

I've only starting learning DL a month ago so mostly have been doing simple ANN. But inferencing larger param NLP models, GANS, Diffusion models, etc is fine. It's no desktop 3090s or enterprise grade GPU but for a laptop it's by far the best on the market. For example, the largest Parque file I've cleaned in pandas was about 7 million rows and about 10gb in size of just text. It can run queries through it in a few seconds.

Guess it depends on what kind of data science or dl you're looking to do. The 3080s probably won't be able to fine tune something like BLOOM model but can fine tune stable diffusion models with enough optimizations.

For modeling in blender or procedural generation in something like Houdini, I haven't had issues. I've made procedurally generated 20km height maps in Houdini to export to Unreal Engine and was not a problem.

>. For example, the largest Parque file I've cleaned in pandas was about 7 million rows and about 10gb in size of just text. It can run queries through it in a few seconds.

Using rapids? Like cudf?

No. I was just using pandas (cpu) for simple quick regex and removing and replacing text rows. It was just for a hobby project. The data was scraped from Midjourney and Stable diffusion discord so there were millions of rows of duplicate prompts and poor quality prompts which I had pandas delete and in the end the number of unique rows with more than 50 characters amounted to about 700k which was then used to train gpt-neo 125m.

I didn't know about cudf. Thanks 😅