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79 lines
3.0 KiB
TeX
79 lines
3.0 KiB
TeX
% Hanzo Consensus AI: Consensus Mechanisms for Distributed AI
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\documentclass[11pt]{article}
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\usepackage{../shared/hanzocover}
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\usepackage{amsmath, amssymb, amsthm}
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\usepackage{mathtools}
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\usepackage{bm}
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\usepackage{graphicx}
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\usepackage{booktabs}
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\usepackage{hyperref}
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\usepackage{enumitem}
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\usepackage{algorithm}
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\usepackage{algpseudocode}
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\usepackage{xcolor}
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\usepackage{float}
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\usepackage{multirow}
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\hypersetup{colorlinks=true,linkcolor=black,citecolor=blue,urlcolor=blue}
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\newtheorem{theorem}{Theorem}
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\newtheorem{lemma}[theorem]{Lemma}
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\newtheorem{proposition}[theorem]{Proposition}
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\newtheorem{corollary}[theorem]{Corollary}
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\newtheorem{definition}{Definition}
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\newtheorem{remark}{Remark}
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\newtheorem{assumption}{Assumption}
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\title{HANZO CONSENSUS AI\\[0.3em]
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{\large Consensus Mechanisms for\\Distributed AI Training and Inference}}
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\author{
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Hanzo AI Research \\
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\textit{Hanzo Industries Inc (Techstars '17)} \\
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\texttt{[email protected]}
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}
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\date{March 2026}
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\begin{document}
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\hanzocoverpage
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% ============================================================================
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% ABSTRACT
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% ============================================================================
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\begin{abstract}
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We present the consensus mechanisms underlying Hanzo ACI (AI Chain
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Infrastructure), a decentralized compute network where GPU operators serve AI
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models and earn HANZO tokens for verified work. Unlike blockchains that achieve
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consensus on transaction ordering, ACI must achieve consensus on computational
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correctness: did a GPU node actually run the claimed model on the claimed input
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and produce the claimed output? We introduce three novel consensus components:
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(i)~Proof of AI Work (PoAI), a probabilistic verification protocol where
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verifier nodes re-execute a random subset of inference requests to detect
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dishonest workers, (ii)~GPU Cluster Consensus, a protocol for coordinating
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distributed training and tensor-parallel inference across untrusted GPU nodes
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with Byzantine fault tolerance, and (iii)~Inference Attestation, an on-chain
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commitment scheme that binds inference outputs to specific model versions, input
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hashes, and GPU node identities. We prove that PoAI detects cheating with
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probability $1 - (1-p)^k$ where $p$ is the sampling rate and $k$ is the number
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of verification rounds, and that the economic cost of cheating exceeds the
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expected profit under the HANZO staking and slashing
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mechanism~\cite{hanzotokenomics}. The system is deployed on Hanzo ACI with 240
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GPU nodes, processing 47M inference requests per month with a measured cheating
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detection rate of 99.97\% for a 5\% sampling rate.
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\end{abstract}
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% ============================================================================
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\input{sections/01-introduction}
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\input{sections/02-system-model}
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\input{sections/03-proof-of-ai-work}
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\input{sections/04-gpu-cluster-consensus}
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\input{sections/05-inference-attestation}
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\input{sections/06-distributed-training-consensus}
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\input{sections/07-integration-with-lux-consensus}
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\input{sections/08-production-results}
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\input{sections/09-related-work}
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\input{sections/99-conclusion}
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\end{document}
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