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dist-epochtoday at 12:38 PM2 repliesview on HN

Co-founder and Chief Compute Officer Anthropic:

> I think the thing that we were thinking about originally was whenever we're bringing up a new hardware platform, it's a big effort. It's like a huge thing. And so as we were thinking about this, we started doing our own evaluation of MI 355. You guys generously got us a rack to start working. And we expected this to be kind of a big process.

> Our actual experience was we had one engineer who start doing it. They spun up Claude, asked it, hey, bring up this machine, left it going over the weekend. And we ended up with a graph of the actual performance of our leading model on it, just going up and up and up over the weekend.

https://x.com/austinsemis/status/2080336781782753635


Replies

stevefan1999today at 1:30 PM

That only works if you have all the unit tests and making sure the behaviors between different GPUs are expected, and can tolerate difference in implementation details such as numerical stability and complexity for some functions. AMD and Nvidia are doing vastly different computation for different implementation to the same algorithm for their APIs (can't tell due to NDA, but one algorithm Nvidia did it using broadcast, while AMD is using tile), and that's one of the reason I gave up. And perhaps only for light kernels doing basic BLAS operations and GEMMs. With a standard to follow, that's one of places where it indeed is giving it a good chance, for example on Tensorflow with ROCm or PyTorch. You basically have an insurance of the behavior and a translation layer for ROCm and CUDA by Python.

But anything other than that, nah.

Edit: Maybe I remember what algorithm that is. It is a trick to do coding theory stuff by using NTT (Number Theoretic Transform, the friend of DFT, the Discrete Fourier Transform, but on a ring and hence applicable to finite field) on GPU to do computation over a GF(2^8) field, for my former employment but I went back to using GFNI. Still I have a NDA to adhere to and can't really explain what happens.

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paoliniluistoday at 12:45 PM

Using AI slop in software that runs ML models is like buying a Ferrari with a motorbike engine. Might be good for demos or as a proof of concept, but that can’t be used for serving LLMs at scale

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