Hacker News Reader

Comment by Figs | Hacker News Reader

> it's better anyways and surely close to a decade after coming out, we'd expect devices to support it well enough.

A lot of people, myself included, are still using quite old hardware. The GPU in my daily driver is ~10 years old at this point. Between crypto, COVID, and this AI craze raising GPU costs by insane amounts, it hasn't made sense to replace it with something newer. I know I'm not alone on that...

View on HN

For legacy devices, VP8/VP9 is a good option. Intel Added VP8 hardware decoding to Broadwell which was 12 years ago. Nvidia had hardware VP9 decoding 10 years ago on the Geforce 10 series. AMD had hardware VP9 decode support 9 years ago on the Radeon 400 series.

If my 10 year old card can't encode in hardware, it's a nonstarter.

I still happily use my 2012 27" iMac for all my work. So I'm with the parent comment.

I work in AI and I'm surrounded by RTX-4090 and H100 servers but for much of the day to day AI training I use my RTX-970 in the desktop on my desk for convenience and it works just fine for most cases.

Literally 2 meters from my desk is a 2x RTX-4090 server and many times I just use my 8 year old GPU anyway so you don't need it.

For a long time I thought my RTX-2060 was just not capable and the other day I did a ffmpeg GPU transcode and was surprised by how well it did. So now I am thinking about putting on some of Google's new Gemma edge models (probably the smallest will work with my 6GB VRAM + 2 GB) setup. I am not a 100% sure what that 2GB is but I think it is borrowing from the system in some manner.

Video encoding uses dedicated silicon, it's not using the card's compute.

Reply on news.ycombinator.com