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The surge in large language models (LLMs) has set high expectations for intelligent, blockchain-integrated decentralized apps, posing significant challenges due to AI’s demand for vast, typically centralized computational power. To address this, developers are innovating in the crypto sphere, establishing peer-to-peer GPU networks to offer essential, decentralized, and affordable GPU resources. This groundwork supports a growing crypto AI environment. On top of this GPU infrastructure, builders are creating inference networks integrated with various blockchain ecosystems. Zero-knowledge machine learning (ZKML) plays a crucial role in ensuring the decentralization and integrity of model inferences.
ML models are inherently vulnerable; altering just one weight among billions of parameters can lead to inaccurate results. Robust solutions are needed to verify the correctness of ML inferences before widespread adoption. Further advancements include decentralized networks with incentives inspired by the NFT market and advanced stablecoins yielding profits for AI users, promoting an economy where funds earn interest when not deployed for AI services. Beyond Ethereum, this AI revolution is extending to Bitcoin, with developers crafting fast, cost-efficient, programmable Layer 2 solutions, gearing up for an expansive AI wave across blockchain ecosystems.