IMO, this is something that makes sense for PyTorch to release, as "neutral ground" in the industry.

One of the main values of Triton is that it significantly expanded the scope of folks who can write kernels - I think Helion could expand the scope even more.

The component about requiring long context lengths to be compute-bound for attention is also quite misleading.

If you compute out the MFU the author gets it's 1.44 million input tokens per second * 37 billion active params * 2 (FMA) / 8 [GPUs per instance] = 13 Petaflops per second. That's approximately 7x absolutely peak FLOPS on the hardware. Obviously, that's impossible.

There's many other issues with this article, such as assuming only 32 concurrent requests(?), only 8 GPUs per instance as opposed to the more efficient/standard prefill-decode disagg setups, assuming that attention computation is the main thing that makes models compute-bound, etc. It's a bit of an indictment of HN's understanding of LLMs that most people are bringing up issues with the article that aren't any of the fundamental misunderstandings here.

1. The academy has had a significant increase of young voters in the past 10 years or so. Generally speaking, young voters are more likely to take animation as a "serious" medium.

2. These interviews were always somewhat overstated. Of course some voters have stupid rationales, but I don't think this dominates the academy.

3. Disney's Inside Out 2 was nowhere close to winning the award this year - Flow's biggest competition was The Wild Robot, which did gross far more than Inside Out 2, but far below Inside Out 2.

If you look at the past couple years, The Boy and the Heron (Studio Ghibli) won over Across the Spider-Verse (with Pixar's movie Elemental nowhere close) in 2023, Guillermo del Toro's Pinocchio won over Across the Spider-Verse (with Pixar's movie Turning Red nowhere close) in 2022, etc.

I'm curious what year you're thinking about above. Perhaps Toy Story 4 over Klaus in 2019?

Which takes it from

> Broadcom generates a 70% profit margin from its work on TPUs, said a person with direct knowledge of the internal analysis. SemiAnalysis, a chip research firm, earlier reported that figure.

https://semianalysis.com/2023/08/30/broadcoms-google-tpu-rev...

Google certainly has infra/cost advantages, but it's nowhere near 10x.

Perhaps if XLA generated all functions from scratch, this would be more compelling. But XLA relies very heavily on pattern-matching to common library functions (e.g. CuDNN), and these patterns will certainly work better on Nvidia GPUs than AMD GPUs.

In this way, I actually think explicitly calling the common library functions is actually much more transparent.