How Decentralized Cloud Computing is Shaping the AI Era

Human social development often takes a dramatic turn with the advent of a few crucial scientific inventions and advancements. Each technological breakthrough ushers in a new era of efficiency and prosperity.

The Industrial Revolution, the Electrical Revolution, and the Information Revolution are landmark advancements in human history. These revolutions fundamentally transformed society, leading to unprecedented changes in productivity and lifestyle. We can no longer imagine returning to the days of oil lamps for lighting or horse-drawn carriages for communication. With the emergence of large language models (LLMs), humanity is entering yet another transformative era.

LLMs are progressively liberating human intelligence, enabling individuals to focus their limited energy and intellect on more creative endeavors and practical applications. This leads to a more efficient world.

We consider GPT a pivotal technological breakthrough due to its significant advancements in natural language understanding and generation and because it demonstrates the pattern of LLM capability growth. By continuously expanding model parameters and training data, LLMs can achieve exponential improvements in capability. With sufficient computing power, this process currently appears to have no discernible bottlenecks.

source: https://arxiv.org/pdf/2202.05924.pdf,https://developers.io.net/docs/how-we-started

The utility of large language models (LLMs) extends far beyond understanding human language and conversation; this capability is merely the beginning. With machines gaining language understanding, a Pandora’s box of endless possibilities opens, allowing people to leverage AI for developing various disruptive functionalities.

Currently, LLM models are advancing across diverse interdisciplinary fields. From the humanities, such as video production and artistic creation, to hard sciences like drug development and biotechnology, monumental changes are imminent.

In this era, computing power is a scarce and valuable resource. Large tech giants possess abundant resources, while emerging developers struggle to compete due to insufficient computing capabilities. In the AI era, computing power equates to strength, and those who control it hold the power to change the world. GPUs, fundamental to deep learning and scientific computing, are crucial in this landscape.

As the field of artificial intelligence (AI) rapidly evolves, it is important to recognize the dual aspects of development: model training and inference. Inference involves the functionality and outputs of AI models, while training encompasses the complex process of building intelligent models, including machine learning algorithms, datasets, and computing power.

Consider GPT-4, for example. Developers need comprehensive foundational datasets and immense computing power to achieve high-quality inference to train effective AI models. These resources are primarily concentrated in the hands of industry giants like NVIDIA, Google, Microsoft, and AWS.

The high cost of computation and barriers to entry hinder many developers from entering the field, perpetuating the dominance of major players. These giants possess large-scale foundational datasets and abundant computing power, enabling them to continuously scale up and reduce costs, thus reinforcing industry barriers.

However, blockchain technology has potential solutions to reduce computing costs and industry entry barriers. Decentralized distributed cloud computing offers precisely such a solution in this era.

The Feasibility and Necessity of Decentralized Cloud Computing

Supply Issues: Low GPU Utilization

Despite the high cost and scarcity of computing power, GPUs are often underutilized. This is primarily because an established method has not been established to integrate these dispersed computing resources and make them commercially viable. Here are the typical GPU utilization figures for various workloads:

Most consumer devices with GPUs fall into the following categories:

• Idle (just booted into the Windows operating system): 0–2% GPU utilization
• General productivity tasks (writing, simple browsing): 0–15% GPU utilization
• Video playback: 15–35% GPU utilization

These figures indicate extremely low utilization of computational resources. In the Web2 world, no effective measures exist to collect and integrate these resources. However, the crypto and blockchain economy may provide the perfect solution. The crypto economy creates an exceptionally efficient global market, where the unique token economy and decentralized systems enable highly efficient pricing, circulation, and matching of market supply and demand for resources.

Edge Computing

The development of AI is shaping humanity’s future, with the progress of computing power driving AI advancements. Since the invention of the first computer in the 1940s, the computing paradigm has undergone several transformations. From bulky mainframe computers to lightweight laptops and from centralized server purchases to computing power leasing, the barriers to accessing computing power have gradually decreased. Before cloud computing, enterprises had to purchase their own servers and continually upgrade them as technology advanced. The emergence of cloud computing, however, completely revolutionized this model.

The basic concept of cloud computing is that consumers lease servers, access them remotely, and pay based on usage. Traditional enterprises are now being disrupted by cloud computing. At the core of cloud computing is virtualization technology, which allows a powerful server to be divided into smaller, rentable units and dynamically allocates various resources.

This model has fundamentally transformed the business landscape of the computing power industry. Previously, individuals and companies needed to purchase computing facilities to meet their needs. Now, they only need to pay for rentals online to access high-quality computing services. The future direction of cloud computing is edge computing. Traditional centralized systems are often too far from users, resulting in some degree of latency. Although latency can be optimized, it cannot be completely eliminated due to the speed of light limitations.

Emerging industries such as the metaverse, autonomous driving, and remote healthcare require extremely low latency. Consequently, cloud computing servers need to be moved closer to users, leading to the deployment of more small data centers around users. This approach is known as edge computing.

Advantages of Decentralized Edge Computing

Compared to centralized cloud computing providers, decentralized cloud computing offers several advantages:

1. Accessibility and Flexibility: Accessing high-performance computing chips from centralized providers like AWS, GCP, or Azure can take several weeks, and models such as the A100 and H100 are often out of stock. Additionally, consumers usually need to sign long-term, inflexible contracts with these companies, leading to delays and operational inflexibility. In contrast, distributed computing platforms provide on-demand computing power and flexible hardware choices, enhancing accessibility.
2. Lower Cost: Distributed computing networks can offer more affordable computing power by utilizing idle chips and token subsidies from network protocols.
3. Resistance to Censorship: Web3 systems like io.net and Aethir do not operate as permissionless systems. They address compliance issues such as GDPR and HIPAA during GPU onboarding, data loading, data sharing, and result sharing stages.

As AI continues to develop and the supply-demand imbalance for GPUs persists, more developers will be driven toward decentralized cloud computing platforms. Additionally, during bullish market periods, the rise in cryptocurrency prices will lead to higher profits for GPU suppliers, encouraging more providers to enter the market and creating a positive feedback loop.

Challenges of Decentralized Edge Computing

1. Parallelization Challenges: Distributed computing platforms often aggregate long-tail chip supplies, meaning that a single chip supplier is typically unable to independently complete complex AI model training or inference tasks quickly. To remain competitive, cloud computing platforms must use parallelization to break down and distribute tasks, reducing overall completion time and enhancing computing power.
2. However, parallelization presents several challenges, including task decomposition (particularly for complex deep learning tasks), managing data dependencies, and additional communication costs between devices.
3. Risk of New Technology Substitution: Significant capital investment in ASIC (Application-Specific Integrated Circuit) research and new inventions like Tensor Processing Units (TPUs) could impact the GPU clusters of decentralized computing platforms. Suppose these ASICs offer good performance at a balanced cost. In that case, the GPU market, which is currently dominated by large AI organizations, may shift, increasing GPU supply and affecting the ecosystem of decentralized cloud computing platforms.
4. Regulatory Risks: Decentralized cloud computing systems operate across multiple jurisdictions, each with its own legal regulations, leading to unique legal and regulatory challenges. Compliance with data protection and privacy laws can also be complex and challenging.

Currently, the main users of cloud computing platforms are professional developers and institutions who prefer long-term stability and are unlikely to switch platforms arbitrarily. For these users, service stability is a greater priority than price. Therefore, decentralized platforms with strong integration capabilities and stable computing power are more likely to attract these customers, leading to long-term partnerships and stable cash flow.

Spheron: On-Demand DePIN for GPUs

While it’s often stated that the demand for GPU power exceeds supply, this narrative overlooks the vast reservoir of underutilized GPUs outside centralized computing services. Many GPUs sit dormant, not because they aren’t needed, but because they aren’t accessible through traditional channels. The key to fueling the future of compute-intensive applications like AI lies in unlocking this immense computational potential — precisely what Spheron lets you do.

Whether you are an individual GPU owner with a single high-performance card or run a data center with extensive resources, Spheron’s innovative platform presents a lucrative opportunity to monetize your hardware.

What is Spheron?

Spheron’s decentralized architecture is built to maximize the utilization of your GPU resources. By joining the Spheron network, your equipment becomes part of a global compute framework, facilitating access to a wide market. This system is underpinned by Spheron’s Decentralized Compute Network (DCN), which ensures that all resource allocations are efficient and secure, optimizing your GPU’s workload without compromising lifespan.

Key benefits include:

• Passive Returns: Earn by renting out excess GPU resources, thereby extending the economic life of your computational resources.
• Global Reach: Access a worldwide market of users, expanding your potential user base without the need for manual outreach or deployment.
• Flexibility: Ability to adjust offerings in real-time based on demand and pricing.
• Transparent and Fair Marketplace: Spheron’s blockchain-based architecture ensures transparency in transactions, pricing, and resource allocation, fostering a fair and open marketplace for compute resources.
• Enhanced Security: Spheron takes security to the next level by utilizing Actively Validated Services (AVS), enhancing security, and restricting unauthorized access to private information from both the host and user sides.
• Participation Rewards: Earn additional income based on a wide range of performance-based milestones, including consistent uptime and quality service.

In short, Spheron’s decentralized platform offers a multitude of benefits tailored to GPU providers of all scales. By integrating your resources with Spheron, you tap into a global demand for computational power, enabling higher GPU utilization and passive revenue streams.

How Spheron Works

Spheron’s GPU providers are automatically matched with end users via an Eigenlayer AVS-based matching engine, which allocates resources based on:

• Region/Availability Zone: Matches based on geographical proximity to reduce latency and comply with local data laws.
• Price Delta: Aligns user budgets with provider bids for cost-efficiency.
• Uptime/Availability: Prefers providers with reliable service histories.
• Reputation: Considers providers’ past performance and standing within the network.
• Resource Availability: Matches based on providers’ current capacity to meet demand.
• Slash Rate: Takes into account any penalties providers have received for contract breaches.
• Token Stakes: Favors providers who invest more in the network, enhancing their chances of selection.
• Randomness: Adds unpredictability to the selection process to prevent implicit biases

In addition to the above, Spheron’s platform ensures that all interactions are secured with advanced smart contracts, guaranteeing transaction transparency and timely payments. In order to streamline this entire process, Spheron utilizes Layer 2 scaling solutions such as the Arbitrum Orbit stack to significantly reduce operational costs and increase transaction speed, ultimately impacting your earnings.

A Place for Every Provider

Spheron recognizes the diversity in the GPU provider community and offers structured tiers catering to various resource availability levels. If you’re considering contributing your GPU resources, here’s a breakdown of the provider tiers that might suit your setup:

• Entry Tier: Ideal if you have GPUs priced below $1,000, suitable for basic model inferencing, offering modest performance.
• Low Tier: Best for GPUs under $2,000, fit for less demanding machine learning tasks and inferencing.
• Medium Tier: Perfect for GPUs under $5,000, commonly used in commercial applications for distributed training and model inferencing.
• High Tier: If you own premium GPUs over $7,500, these are great for training large language models and handling other intensive tasks.
• Ultra-High Tier: For those with GPUs over $15,000, designed for the most demanding tasks in training large language models and other intensive computational needs.

Each tier is structured to ensure that, regardless of the size of your operations, you can play an active role within Spheron’s ecosystem and earn passive income from your resource contributions.

Start Earning with Spheron

Choosing Spheron means more than just additional income; it’s about becoming part of a cutting-edge technological ecosystem, reshaping how computational resources are distributed and utilized globally. The platform not only supports your business model but also contributes to a broader ecosystem that promotes innovation and development across multiple fields, including AI and machine learning.

Rather than sit on idle GPU power, you’re better off leveraging your untapped resources to power a new era of innovation. Whether you’re looking to make the most out of your idle GPUs or want to directly contribute to growing fields like AI and machine learning, Spheron has a place for you.

Learn more in Spheron’s v1 white paper.

Originally published at https://blog.spheron.network on May 27, 2024.