Aethir: A decentralized cloud computing player with triple track strength

The development and advancement of LLM large models and AI is an extremely great technological progress in human history. Humanity has now entered the AI era, where "computing power" is the most scarce resource in this new world.

The trend of computing power development is edge computing, which can effectively reduce physical latency and become the cornerstone for the development of industries with low latency requirements such as the metaverse; decentralized distributed cloud computing has advantages of flexibility, low cost, and resistance to censorship, with a very broad development prospect.

Aethir is a decentralized real-time rendering platform based on the Arbitrum network, providing enterprise-level computing power services for gaming, artificial intelligence, and other businesses by aggregating high-performance GPUs such as the H100.

Aethir has already partnered with top cloud computing projects in the industry such as io.net and Theta, as well as several leading game studios and telecom companies. It is expected that the annual recurring revenue for the first quarter of 2024 will exceed $20 million.

Aethir Edge significantly lowers the threshold for ordinary users to sell excess computing power and greatly expands the geographical coverage of the Aethir network.

Aethir has raised $80 million through the sale of checker node NFTs, proving that its project prospects and economic model are highly attractive to a wide range of users.

The hourly usage cost of Aethir Network's A100 is significantly lower than that of other competitors, providing a clear competitive advantage.

The changes in the development process of human society are often achieved through several extremely great scientific inventions and advancements. Each breakthrough in technology will directly create a more efficient and prosperous new era.

The Industrial Revolution, the Electrical Revolution, and the Information Revolution are some of the greatest technological advances in human history. They have completely transformed the face of human society, bringing unprecedented changes in productivity and lifestyle. Now, we can no longer return to the era of kerosene lamps for lighting and horse-drawn carriages for delivering letters. With the birth of GPT, humanity has entered another great new era.

LLM is gradually liberating human intelligence, allowing people to use their limited energy and intelligence for more creative thinking and practice, leading humanity into a more efficient world.

We see GPT as another technological breakthrough that changes the world, not only because of the tremendous progress GPT has made in natural language understanding and generation, but also because humanity has grasped the规律 of the growth of large language model capabilities in the evolution of GPT ------ that is, by continuously expanding model parameters and training data, we can achieve exponential improvements in the capabilities of LLM models. Under sufficient computing power, this process currently shows no signs of bottleneck.

The use of large language models is not limited to understanding human language and dialogue; rather, this is just the beginning. Once machines possess the ability to understand language, it's like opening a Pandora's box, releasing an infinite space for imagination. People can leverage this capability of AI to develop various disruptive functions.

At present, in various cross-disciplinary technological fields, LLM models have already begun to showcase their capabilities. From video production and artistic creation in the humanities to drug development and biotechnology in hard technology, a revolutionary change is bound to occur.

In this era, computing power is seen as a scarce resource, with large tech giants holding abundant resources, while emerging developers face barriers to entry due to insufficient computing power. In the new AI epoch, computing power equates to strength; those who possess computing power have the ability to change the world. GPUs, as the cornerstone of deep learning and scientific computing, play a crucial role in this.

In the rapidly evolving field of artificial intelligence (AI), we must recognize the dual aspects of development: model training and inference. Inference involves the functionality and output of AI models, while training encompasses the complex processes required to build intelligent models, including machine learning algorithms, datasets, and computational power.

Taking GPT-4 as an example, if developers want to obtain high-quality reasoning, they need to acquire comprehensive foundational datasets and immense computational power to train effective AI models. These resources are primarily concentrated in the hands of industry giants such as NVIDIA, Google, Microsoft, and AWS.

The high computing costs and entry barriers prevent more developers from entering, which also strengthens the leading players. They possess large foundational datasets and significant computing power, with the ability to continuously scale up and reduce their own costs, which further solidifies the industry barriers.

But we can't help but wonder if there are solutions to reduce computing costs and industry entry barriers by adopting blockchain technology. The answer is yes. Decentralization distributed cloud computing provides us with such a solution in this era.

Despite the current situation where computing power is expensive and scarce, GPUs have not been fully utilized. This is mainly because a ready-made way to integrate these decentralized computing powers and operate them in a commercial manner has not yet emerged. Below are typical GPU utilization figures for different workloads:

Most consumer devices with GPUs belong to the first three categories, that is, idle ( just started entering the Windows operating system ):

• GPU utilization is: 0-2%;
• General production task ( writing, simple browsing ):0-15%;
• Video playback: 15 - 35%.

The above data indicates that the utilization of computational resources is extremely low, and there are no effective measures in the Web2 world to collect and integrate these resources. However, Crypto and the blockchain economy may be just the remedy to address this challenge. The crypto economy constructs a highly efficient global market, and due to its unique token economy and characteristics of decentralization, the pricing, circulation, and matching of market supply and demand for resources are very efficient.

The development of AI is influencing the future of humanity, and the advancement of computing power determines the development of AI. Since the invention of the first computer in the 1940s, computing models have undergone multiple transformations. From bulky mainframe computers to lightweight laptops, from purchasing centralized servers to renting computing power, the threshold for obtaining computing power is gradually decreasing. Before the advent of cloud computing, companies had to procure servers themselves and continuously update them with technological innovations, but the emergence of cloud computing has completely changed this model.

The basic concept of cloud computing is that the demand side rents servers, accesses them remotely, and pays based on usage. Now, traditional enterprises are being disrupted by cloud computing. In the field of cloud computing, virtualization technology is at the core. Virtualized servers can partition a powerful server into smaller servers for rental and can dynamically allocate various resources.

This model has completely changed the business landscape of the computing power industry. In the past, people needed to purchase computing power facilities themselves to meet their computing power needs; but now, they only need to pay rent on a website to enjoy high-quality computing power services. The future development direction of cloud computing is edge computing. Due to the distance of traditional centralized systems from users, this can lead to a certain degree of latency. Although latency can be optimized, it can never be completely overcome due to the limitations of the speed of light.

However, emerging industries such as the metaverse, autonomous driving, and telemedicine have extremely low latency requirements. Therefore, it is necessary to relocate cloud computing servers closer to users, leading to an increasing number of small data centers being deployed around users. This is edge computing.

Compared to centralized cloud computing providers, the advantages of decentralized cloud computing mainly lie in:

• Accessibility and Flexibility: Accessing computing power chips on cloud service platforms such as AWS, GCP, or Azure usually takes several weeks, and high-performance GPU models like A100 and H100 are often out of stock. Additionally, to obtain computing power, consumers typically need to sign long-term, inflexible contracts with these large companies, which not only results in a loss of time but also makes business operations rigid, losing a degree of flexibility. In contrast, distributed computing power platforms allow for on-demand access to computing power and provide flexible hardware options, offering greater accessibility.
• Lower Prices: Due to the use of idle chips, combined with the token subsidies from network protocol parties to chip and computing power suppliers, the distributed computing power network may be able to offer cheaper computing power.
• Anti-censorship: Some systems do not position themselves as permissionless systems. Compliance issues such as GDPR and HIPAA are addressed during the GPU launch, data loading, data sharing, and results sharing phases.

With the further development of AI and the ongoing imbalance in GPU supply and demand, more developers will be driven to turn to decentralized cloud computing platforms. At the same time, during a bull market, the rising prices of cryptocurrency tokens will allow GPU suppliers to earn more revenue, which will encourage more GPU providers to enter this market, creating a positive feedback loop.

(# Technical Challenges

1. Parallelization Problem

Distributed computing platforms typically aggregate a long tail of chip suppliers, which means that individual chip providers are almost unable to independently complete complex AI model training or inference tasks in a short period of time. If cloud computing platforms want to be competitive, they must break down and distribute tasks through parallelization methods to shorten the total completion time and enhance the platform's computing power.

However, the parallelization process will face a series of problems, including how to decompose tasks ), especially for complex deep learning tasks ###, data dependencies, and additional communication costs between devices.

2. New Technology Substitution Risk

With a large capital investment in ASIC( dedicated integrated circuit) research and new inventions like tensor processing units( TPU), there may be an impact on the GPU clusters of decentralized computing platforms.

If these ASICs can provide good performance and have a cost balance, the GPU market currently monopolized by large AI organizations may return to the market. This will lead to an increase in GPU supply, thereby affecting the ecosystem of decentralized cloud computing platforms.

3. Regulatory Risk

Due to the operation of decentralized cloud computing systems across multiple jurisdictions, and the potential to be subject to different laws and regulations, there may be unique legal and regulatory challenges. Compliance requirements, such as data protection and privacy laws, may also be complex and challenging.

At this stage, the users of cloud computing platforms are mainly professional developers and institutions. They prefer to use one platform for a long time and are unlikely to change arbitrarily. Whether to use a decentralized platform or a centralized platform, price is only one of the considerations; these users place greater emphasis on the stability of the service. Therefore, if a decentralized platform has strong integration capabilities and stable and sufficient computing power, it is more likely to win the favor of these clients, establish long-term cooperative relationships, and achieve stable cash flow income.

In this section, I will introduce the new distributed computing project Aethir that focuses on game rendering and AI during this cycle, and based on the current market AI projects and distributed computing projects in the same track, estimate the possible valuation after going public.

Aethir Cloud is a decentralized real-time rendering platform based on the Arbitrum network, which helps gaming and artificial intelligence companies deliver their products directly to consumers by aggregating and intelligently redistributing new and idle GPUs from enterprises, data centers, cryptocurrency mining operations, and consumers.

One of the key innovations of the project is the resource pool, which gathers decentralized computing power contributors under a unified interface to provide services to global clients. A significant feature of the resource pool is that GPU providers can freely connect or disconnect from the network, allowing businesses or data centers with idle equipment to participate in the network during downtime, thereby enhancing the flexibility and equipment utilization of suppliers.

The operation of the Aethir ecosystem relies on three core infrastructures:

• Container (: The key function of the container is to provide real-time remote rendering services, offering a "zero latency" experience. The container is the actual location of cloud computing, acting as a virtual endpoint to execute and render applications. This shifts the workload from local devices to the container.
• Checker ): The checker node checks the Container and its service processes.