According to @AndrewYNg, GPUs are pivotal to AI workloads, reinforcing the central role of graphics accelerators in the AI compute cycle. Source: https://twitter.com/AndrewYNg/status/1983609014408851718 NVIDIA’s data center GPUs (H100, H200) and the Blackwell platform are built for large-scale training and inference, making NVDA a primary market proxy for AI GPU demand. Sources: https://www.nvidia.com/en-us/data-center/h100/ ; https://www.nvidia.com/en-us/data-center/h200/ ; https://www.nvidia.com/en-us/data-center/technologies/blackwell/ AMD’s Instinct MI300 series targets AI training and inference, positioning AMD as an alternative AI accelerator supplier and adding competitive GPU capacity to the market. Source: https://www.amd.com/en/products/accelerators/instinct/mi300 The Ethereum Merge in September 2022 shifted ETH from proof-of-work to proof-of-stake, ending ETH GPU mining and decoupling ETH network security from GPU hardware. Source: https://ethereum.org/en/roadmap/merge/ Decentralized compute projects leverage GPUs for rendering and AI workloads, including Render Network (RNDR) and Akash Network (AKT), which document GPU-powered decentralized services and tokenized resource markets. Sources: https://docs.rendernetwork.com/ ; https://docs.akash.network/