# Introduction

The AI market is facing significant challenges arising from shortages in computational power and the complexities associated with the widespread deployment and access to large-scale AI applications. As AI technologies, particularly advanced models such as deep learning and large language models, continue to evolve, their demand for extensive computational resources has escalated. The training and operation of these sophisticated systems necessitate substantial processing power, memory, and energy, which are often prohibitively costly for smaller companies and individual users. This growing demand for high-performance computing has created a considerable bottleneck, limiting access to cutting-edge AI technologies to those with significant resources. Consequently, many potential users cannot fully leverage the capabilities of advanced AI systems.

The deployment of large-scale AI applications introduces additional layers of complexity. These systems are resource-intensive and require advanced technical expertise and a robust infrastructure to integrate effectively with existing platforms. This represents a significant barrier for many businesses and individuals, as deploying AI solutions often entails high costs, ongoing technical support, and specialized knowledge that may not be readily accessible. The challenge of simplifying and streamlining the deployment process remains a major obstacle, limiting the widespread adoption of AI applications by non-expert users and smaller organizations.

The rapid proliferation of AI applications in recent years has compounded these challenges. The sheer volume of AI solutions entering the market has led to significant fragmentation, with many tools lacking interoperability. As a result, users often face difficulties integrating different AI applications across platforms, leading to inefficiencies and a fragmented experience. Moreover, the pace of innovation in AI technologies has outstripped efforts to establish standardization, further hindering the seamless deployment and integration of these tools.

To address the aforementioned challenges, we propose the zkAgent project, with the following key contributions:

1. We have designed a decentralized framework based on Function-as-a-Service (FaaS), allowing users to deploy AI applications within a highly distributed computing network.
2. We have created a Real World Asset (RWA) network that leverages the Tendermint consensus and employs a Proof-of-Physical-Work (PoPW) mechanism for scheduling computing nodes, task evaluation, and reward distribution. This approach ensures transparency, fairness, and operational efficiency across the network.
3. We provide a high-performance SDK for rapid AI application deployment, enabling users to quickly deploy complex AI applications directly within the distributed computing network.

In the following sections, we will present the background, related works, framework, experiments, and tokenomics of zkAgent in detail.


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