DFT Intern

A New Type Of ComputingWith the last few years of exponential progress in AI technology, major winners have been the builders of AI hardware. This is because modern AI, mostly using neural network technology, uses computing power in a very different way from classical computers.Instead of performing complex calculations with a powerful CPU, they instead perform thousands or millions of simpler calculations in parallel.(You can learn more about how neural networks were invented and work in “Investing in Nobel Prize Achievements – Artificial Neural Networks, The Basis Of AI”)So far, graphic cards, or GPUs (Graphics processing units), have been the favored tool to develop AI, dramatically boosting the revenues and profits of leaders in the sector like Nvidia (NVDA +0.39%).The market for AI hardware is expected to keep growing exceptionally quickly, at 31.2% CAGR from 2025 to 2035This period should also see the emergence of many new types of AI hardware, as the GPUs repurposed for AI calculation are progressively replaced by chips designed specifically for this application.In the long run, more exotic forms of computing are likely to make their way into the AI hardware market, from application-specific designs to non-silicon chips or even using actual biological neurons.How AI Thinking WorksThe fundamental difference between classical supercomputers and AIs is how data is processed. Instead of solving complex calculations, neural networks create virtual nodes connected into a network. While the initial neural network contained barely a few dozen nodes, making a few hundred connections, modern neural networks like the ones used by ChatGPT use trillions of possible connections, reaching levels of complexity not dissimilar to the human brain. This different method of calculus requires hardware able to perform millions of operations in parallel, even if the computing power dedicated to each is relatively small.Luckily, this is a type of hardware that has already been in operation for many years, such as graphic rendering using GPUs, mostly for 3D simulation and videogames, and also uses this type of many small calculations in parallel.This is why the initial (and current) winner of the race to secure enough AI chips has been Nvidia, the leader in the GPU market.Ever Quicker GPUsWith the invention of more efficient algorithms and the quick progress in artificial intelligence they created, the potential applications of AI exploded in the 2020s.This led to an ever-increasing race to secure enough hardware, especially Nvidia GPU in 2023.In parallel, increasing expectations from AI potential applications require ever smarter AI, which itself requires more computing power. And while securing more GPUs was a solution, better GPUs were needed as well.The industry delivered, with a 1,000x growth in performance in less than 8 years.Can It Last?There are signs that progress in GPU performances might soon slow down. First, all the “easy” improvements, like making GPUs bigger and with smaller and denser transistors, are getting maxed out

Decart.ai and Etched.ai recently introduced Oasis, an AI-driven model that generates a fully interactive, real-time open-world experience inspired by Minecraft.

Etched is making waves in the artificial intelligence hardware space with its revolutionary new AI accelerator chip. The Silicon Valley startup, founded in 2022 by Harvard dropouts Gavin Uberti and Chris Zhu, has developed a custom application-specific integrated circuit (ASIC) called Sohu that is purpose-built to run transformer models – the architecture behind today’s most advanced AI systems.Etched transformer ASICS for LLMsEtched claims its Sohu chip can process AI workloads up to 20 times faster than Nvidia’s top-of-the-line GPUs while using significantly less power. With $120 million in fresh funding and partnerships with major cloud providers, Etched is positioning itself as a formidable challenger to Nvidia’s dominance in AI chips.Performance of Sohu vs top GPUs (Etched)Primary Venture Partners and Positive Sum Ventures led the funding round, which included participation from high-profile investors like Peter Thiel, Github CEO Thomas Dohmke, and former Coinbase CTO Balaji Srinivasan. As transformer models continue to drive breakthroughs in generative AI, Etched’s specialized hardware could reshape the landscape of AI computing.Etched’s approach targets the complexities of GPUs and TPUs, particularly the need to handle arbitrary CUDA and PyTorch code, which demands sophisticated compilers. While other AI chip developers like AMD, Intel, and AWS have invested billions into software development with limited success, Etched is narrowing its focus. By exclusively running transformers, Etched can streamline software development for these models.Most AI companies use transformer-specific inference libraries such as TensorRT-LLM, vLLM, or HuggingFace’s TGI