Personally I think the limits with those models is just the amount of information that each weight can hold is limited.

The domain name and the prefix also doesn't make it seem sketch at all whatsoever. tistory.com and gpt4chat makes me think it's trying to abuse SEO

At that point we will run out of data. It will require more data efficient methods.

Now try that on 2-4 monitors. You would be surprised how premium gamers like their hardware. It’s like checking out sports cars but for nerds like me.

people are demanding more and more interactivity in their video games (look at the trend of open worlds). It’s only gonna get bigger.

That or just use the cloud until Nvidia releases a 48gb gpu (which will happen sooner than one would think. Games are getting limited by VRAM)

Nice job

Lol AI ethics probably seems like just paying Philosophers from the prospective of a corporation. There are already plenty on Youtube and social media

Whole purpose is to communicate how their naming isn't matching what they are actually doing.

People can't do deep learning or AI without the tools to make them happen. Imagine how complicated the data collection methods at the scale of Terabytes of data and cleaning it. People also need to annotate data for it to work which requires software to get it done in a cost effective manner.

Ernie comrade what happened to the Dalai Lama?

Ernie: ... USA caused a problem

Ernie why is Russia invading Ukraine?

Ernie: USA started the war

Ernie where is Taiwan?

Ernie: Taiwan? It is in the South China Sea owned by all if China. South Korea is also part of China.

Honestly this is like the movie scene where the hero becomes the villain they once pledge to fight. People have been pulling out because of the direction they are going.

OpenAI naming is like the People's Republic of China. Totally for the people and chosen by the people.

Research pub or Gtfo

Go to the used market. Buying brand new cards will be more expensive because those are scalpers. Second hand is much more reasonable.

You might get a better deal by getting and rtx 3090. Double the VRAM for around the same price. It’s way to easy to hit the VRAM limit on a gpu.

Intelligence is the ability to take complex information into a simple explanation that a child can understand .

It makes me skeptical if someone doesn’t explain besides performance reasons . Most people just use the cloud because ML networks regardless of size take up a lot of battery.

Develop on your PC first then just use it for a train job. If you have to use it longer than the timeout, the just buy a cloud instance. I have been using Colab for years and never got those issues. Use what it’s meant for and you won’t run into problems.

OP's use case though is just looking for a cheap gpu to dabble into. If you have the money for the 3090 then go ahead. However the cloud and Colab is a lot cheaper at the moment until Google decides to screw everyone over in the future.

Use a spot instance. If you testing it out you wallet will thank you later. Look at my previous post on here about running stuff in the cloud before you do it.

What’s the reason to use this over a transformers? Transformers allow transfer learning and is able to paralize easier. 啊我看到你的知乎。你在什么公司去工作？

I suggest using Colab free. The resources are more than most people need and use the cloud when you got a serious work load like a business or research.

If you want to do gaming with that then try the rtx 3060 instead. More VRAM let’s you do more than rtx 3070 ironically.

Either paths will eventually lead you to the cloud to be remotely competitive in serious workloads.

3060 is better. The vram let’s you get more stuff done

You add padding

Use some spot instances on the cloud. It's a lot cheaper than getting a 3k rig unless if you train the model throughout the entire year. You can also connect to a VM on GCP through Colab so everything feels the same, just with a change.

Spot instances and Lambda cloud have super low margins and it isn't the big mark up in traditional products.