Why Nvidia's AI Monopoly is Coming to An End. The Battle For AI Market Domination 9-8-2024 - Dr Waku Unravels the Tangled Web of AI Systems, Methods and Standards, and the Fierce Compition to Outdo All Other Competitors in - - THE QUEST FOR AI WORLD DOMINATION - 64,534 views Sep 8, 2024 Dr Waku - 0:00 Intro 0:24 Contents 0:31 Part 1: Nvidia's Cuda moat 1:16 Evolution of Cuda 2:01 GPGPU programming languages 2:29 What made Cuda powerful? 3:26 The competitor to Cuda: OpenCL 4:06 PTX machine code for Nvidia GPUs evolves rapidly 5:15 Other GPU frameworks output Cuda code 6:22 Example: PyTorch outputs Cuda 6:56 Part 2: The competitors' view 7:29 Companies like to have secondary providers 8:14 Approach 1: Maintain hardware compatibility 9:00 In contrast to Nvidia, Intel has a stable architecture 9:39 Approach 2: Library compatibility 10:48 Approach 3: Binary translation 11:53 Approach 4: Create a new compiler 12:22 Clean room compiler reimplementation 13:48 How Intel is using library compatibility 14:36 AMD also didn't want library compatibility 15:03 Moore Threads, Chinese GPU company 15:40 Moore Threads is doing binary translation? 16:29 In response, Nvidia bans disassembly of code 17:23 Nvidia probably doesn't want binary translation tools 18:09 New compiler: SCALE from Spectral Compute 19:12 Spectral Compute could get bought by AMD 19:35 AMD's compiler ROCm doesn't work well 20:06 AMD acquired Nod.AI to get an optimizing compiler 21:00 Part 3: Should Nvidia be worried? 21:11 Implementing Cuda compatibility is a catch up technique 21:31 Nvidia's profit margins depend on its monopoly 21:56 My perspective: Cuda is too complicated 22:55 Nvidia should be most worried about other stacks 23:49 New stacks are already being created 24:07 PyTorch 2.0 is more AMD friendly 24:44 OpenAI's Triton is an easier to use GPGPU language 25:32 How Triton works, targeting PTX directly 26:20 Why LLVM names Triton so powerful 27:30 Should Nvidia use LLVM too? 28:03 Triton only supports what OpenAI needs 28:58 Yes, Nvidia should be worried 29:37 Are machine learning folks going to use AMD? 30:20 Conclusion 31:15 Bypassing Cuda completely 32:20 Outro - We discuss why Nvidia has a software moat against competitors who might want to manufacture GPUs. Nvidia's Cuda language has had unique advantages over time, including the ability to model memory hierarchy precisely. The fundamental machine learning tools like pytorch ended up supporting only Cuda, cementing Nvidia's position. - There are multiple ways competitors could try to break the Cuda monopoly: hardware compatibility, library compatibility, binary translation, or even creating a new compiler. Companies are starting to engage in these approaches, especially creating new compilers, which is the least legally challenging. In particular, there is already a f FAIR USE FOR EDUCATIONAL PURPOSES Mirrored From: https://www.youtube.com/@DrWaku