The tiny corp raised $5.1M
&gt; There’s a [Radeon RX 7900 XTX 24GB] already on the market. For $999, you get a 123 TFLOP card with 24 GB of 960 GB&#x2F;s RAM. This is the best FLOPS per dollar today, and yet…nobody in ML uses it.<p>&gt; I promise it’s better than the chip you taped out! It has 58B transistors on TSMC N5, and it’s like the 20th generation chip made by the company, 3rd in this series. Why are you so arrogant that you think you can make a better chip? And then, if no one uses this one, why would they use yours?<p>&gt; So why does no one use it? The software is terrible!<p>&gt; Forget all that software. The RDNA3 Instruction Set is well documented. The hardware is great. We are going to write our own software.<p>So why not just fix AMD accelerators in pytorch? Both ROCm and pytorch are open sourced. Isn&#x27;t the point of the OSS community to use the community to solve problems? Shouldn&#x27;t this be the killer advantage over CUDA? Making a new library doesn&#x27;t democratize access to the 123 (fp16-)TFLOP accelerator. You fix pytorch and suddenly all the existing code has access to these accelerators. Millions of people now have  This then puts significant pressure on Nvidia, as they can&#x27;t corner the DL market. But it is a catch-22 because the DL market already is mostly Nvidia so it takes priority. Isn&#x27;t this EXACTLY where OSS is supposed to help? I get Hotz wants to make money, and there&#x27;s nothing wrong with that (it also complements his other company), but the arguments here seem more for fixing ROCm and specifically the pytorch implementation.<p>The mission is great, but AMD is in a much better position to compete with AMD. They caught up in the gamer&#x27;s market (mostly) but have a long way to go for scientific work (which is what Nvidia is shifting focus to). This is realistically the only way to drive GPU prices down. Intel tried their hand (including in supercomputers) but failed too. I have to think there&#x27;s a reason that&#x27;s not obvious to most of us as to why this is happening.<p>Note 1:<p>I will add that supercomputers like Frontier (current #1) do use AMDs and a lot of the hope has been that this will fund the optimization from two places: 1) DOE optimizing their own code because that&#x27;s the machine that they have access to and 2) AMD using the contract money to hire more devs. But this doesn&#x27;t seem to be happening fast enough (I know some grad students working on ROCm).<p>Note 2:<p>There&#x27;s a clear difference in how AMD and Nvidia measure TFLOPS. techpowerup shows AMD at 2-3x Nvidia, but performance is similar. Either AMD is crazy underutilized or something is wrong. Does anyone know the answer?