Hacker News new | past | comments | ask | show | jobs | submit login

While I agree that it's much more effort to get things working on AMD cards than it is with Nvidia, I was a bit surprised to see this comment mention Whisper being an example of "5-10x as performant".

https://www.tomshardware.com/news/whisper-audio-transcriptio... is a good example of Nvidia having no excuses being double the price when it comes to Whisper inference, with 7900XTX being directly comparable with 4080, albeit with higher power draw. To be fair it's not using ROCm but Direct3D 11, but for performance/price arguments sake that detail is not relevant.

EDIT: Also using CTranslate2 as an example is not great as it's actually a good showcase why ROCm is so far behind CUDA: It's all about adapting the tech and getting the popular libraries to support it. Things usually get implemented in CUDA first and then would need additional effort to add ROCm support that projects with low amount of (possibly hobbyist) maintainers might not have available. There's even an issue in CTranslate2 where they clearly state no-one is working to get ROCm supported in the library. ( https://github.com/OpenNMT/CTranslate2/issues/1072#issuecomm... )




> While I agree that it's much more effort to get things working on AMD cards than it is with Nvidia, I was a bit surprised to see this comment mention Whisper being an example of "5-10x as performant".

It easily is. See the benchmarks[0] from faster-whisper which uses Ctranslate2. That's 5x faster than OpenAI reference code on a Tesla V100. Needless to say something like a 4080 easily multiplies that.

> https://www.tomshardware.com/news/whisper-audio-transcriptio... is a good example of Nvidia having no excuses being double the price when it comes to Whisper inference, with 7900XTX being directly comparable with 4080, albeit with higher power draw. To be fair it's not using ROCm but Direct3D 11, but for performance/price arguments sake that detail is not relevant.

With all due respect to the author of the article this is "my first entry into ML" territory. They talk about a 5-10 second delay, my project can do sub 1 second times[1] even with ancient GPUs thanks to Ctranslate2. I don't have an RTX 4080 but if you look at the performance stats for the closest thing (RTX 4090) the performance numbers are positively bonkers - completely untouchable for anything ROCm based. Same goes for the other projects I linked, lmdeploy does over 100 tokens/s in a single session with LLama2 13b on my RTX 4090 and almost 600 tokens/s across eight simultaneous sessions.

> EDIT: Also using CTranslate2 as an example is not great as it's actually a good showcase why ROCm is so far behind CUDA: It's all about adapting the tech and getting the popular libraries to support it. Things usually get implemented in CUDA first and then would need additional effort to add ROCm support that projects with low amount of (possibly hobbyist) maintainers might not have available. There's even an issue in CTranslate2 where they clearly state no-one is working to get ROCm supported in the library. ( https://github.com/OpenNMT/CTranslate2/issues/1072#issuecomm... )

I don't understand what you're saying here. It (along with the other projects I linked here[2]) are fantastic examples of just how far behind the ROCm ecosystem is. ROCm isn't even on the radar for most of them as your linked issue highlights.

Things always get implemented in CUDA first (ten years in this space and I've never seen ROCm first) and ROCm users either wait months (minimum) for sub-par performance or never get it at all.

[0] - https://github.com/guillaumekln/faster-whisper#benchmark

[1] - https://heywillow.io/components/willow-inference-server/#ben...

[2] - https://news.ycombinator.com/item?id=37793635#37798902




Guidelines | FAQ | Lists | API | Security | Legal | Apply to YC | Contact

Search: