The rapid proliferation of AI-powered edge devices necessitates robust benchmarking frameworks to assess performance, fairness, and reliability across diverse environments. dAIEdge-VLab, a collaborative platform for launching benchmarking experiments on remote AI-enabled edge devices, requires a trustworthy and decentralized approach to ensure integrity, transparency, and security. Blockchain technology presents a compelling solution by enabling unchangeable benchmarking experiment reports, decentralized consensus mechanisms, and tamper-proof data management. Through smart contracts, automated execution of benchmarking protocols can enhance reproducibility and fairness while reducing dependence on centralized authorities. Additionally, token-based incentive models can foster active participation and resource-sharing among stakeholders. Despite these advantages, integrating blockchain into remote AI benchmarking poses challenges, including increased computational overhead, scalability constraints, and latency issues. Moreover, the cost of on-chain storage and transaction fees can impact feasibility, requiring hybrid solutions that combine off-chain storage with blockchain validation. This paper explores the opportunities and trade-offs of blockchain integration in dAIEdge-VLab, presenting a novel decentralized framework that balances security, efficiency, and accessibility for AI benchmarking on remote edge nodes. By leveraging blockchain’s strengths while addressing its limitations, we propose an architecture that fosters trust, transparency, and collaboration in AI performance evaluation on edge devices.