🧐Problem

The endeavor to access distributed computing resources via the public cloud is hampered by several significant hurdles.

2.1 Exorbitant Expenses

Acquiring premium GPUs imposes steep financial demands, leading projects to incur monthly expenses in the realm of hundreds of thousands of dollars for training and inference purposes. The rapid ascendancy of large models, combined with their voracious appetite for computational resources, has vastly exceeded the supply capabilities of companies like NVIDIA. Demand for computational power has seen an exponential rise, doubling every few months.

In the era of large-scale models, computational power is not only a vital element but also the most costly. For corporations aiming to develop these large models internally, the financial barrier is immense, requiring an outlay of around $50 million to set up their own data centers. On the other hand, smaller startups are forced into utilizing expensive cloud computing services due to budget constraints.

2.2 Availability Constraints

The process of obtaining necessary hardware through cloud platforms such as AWS, GCP, or Azure can be exceedingly slow, with waits extending to weeks, and sought-after GPU models often being unavailable.

Another concern is the delay of data exchange: The need for AI GPUs to continually exchange data introduces the challenge of network latency. In distributed computing environments, delays can significantly impede the training process, as timely data exchange is critical for leveraging distributed computational resources effectively.

2.3 Limited Options

The choice of GPU hardware is restricted, with limitations impacting the selection of location, security measures, latency, and other essential considerations.

For decentralized computing networks, the hurdles extend far beyond the limitations of slow and unreliable communication networks and the challenges of synchronizing computational states.

2.4 Blending of different GPU resources

One of the core complexities involves accommodating a diverse array of GPU types within the computing environment, each with its own specifications and requirements. This diversity necessitates sophisticated algorithms and infrastructure capable of seamlessly integrating these varied resources for optimal performance.

Specialized GPU Hardware Requirement: AI training, particularly for Large Language Models (LLMs) with billions of parameters, demands intensive computational efforts. The training of these models requires a significant number of Floating Point Operations per Second (FLOPs), which can only be efficiently handled by specialized hardware such as AI GPUs, equipped with specific components like Tensor Processing Units (TPUs). For optimal performance, it is ideal for all GPUs to be homogeneous, ensuring uniform computation levels and facilitating seamless data exchange. However, in a decentralized network, this poses a requirement on participants' GPUs, raising the entry barrier and potentially hindering the utilization of idle computational power due to the high standards and uniformity required.

2.5 Efficient governance and incentive

Moreover, the implementation of economic incentives to motivate participation adds another layer of complexity. Ensuring these incentives are both attractive and fair requires careful balance, particularly in a decentralized context where traditional oversight mechanisms may be absent or significantly altered. This leads to the necessity of developing robust systems to deter and detect participant cheating, a non-trivial task given the anonymous or pseudonymous nature of blockchain participants.

2.6 Security and privacy

Ensuring the security and privacy of the network and its users is paramount, requiring state-of-the-art cryptographic solutions and privacy-preserving techniques to protect sensitive data from unauthorized access or disclosure. Especially, huge user data will be involved in model training. Furthermore, decentralized networks must be resilient against spam attacks and other malicious activities designed to degrade performance or compromise the network's integrity. This demands advanced anti-spam mechanisms and network design strategies that can identify and neutralize such threats effectively, without imposing undue restrictions on legitimate users.

Challenges above highlight the critical need for a solution that can provide accessible, efficient, and economically viable access to computational resources, catering to the escalating requirements of projects involving extensive computational tasks.