In-depth Analysis of Aethir: A Decentralized Cloud Computing Player with Triple Track Strength

The development and progress of LLM large models and AI are extremely significant technological advancements 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 low-latency demand industries such as the metaverse; Decentralization 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 companies 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 telecommunications companies. It is expected that the annual recurring revenue (ARR) will exceed 20 million dollars in the first quarter of 2024.

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, demonstrating that its project prospects and economic model are highly attractive to a wide range of users.

The hourly usage cost of A100 on the Aethir network is significantly lower than other competitors, providing a clear competitive advantage.

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

The Industrial Revolution, the Electrical Revolution, and the Information Revolution are extremely significant technological advancements in human history. They have fundamentally changed the face of human society, bringing about unprecedented productivity and shifts in lifestyles. 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 channel their limited energy and intelligence into 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—namely, that by continuously expanding the model parameters and training data, the capabilities of the LLM model can increase exponentially. In the case of sufficient computing power, this process currently shows no signs of bottleneck.

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

Currently, in various intersecting technological fields, LLM models have already begun to make their mark. From video production and artistic creation in the humanities to drug development and biotechnology in hard technology, we are sure to witness earth-shattering changes.

In this era, computing power is regarded 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 equals strength, and those who possess computing power have the ability to change the world. GPUs, as the cornerstone of deep learning and scientific computing, play a vital role in this.

In the rapidly developing 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, which include machine learning algorithms, datasets, and computational power.

Taking GPT-4 as an example, if developers want to obtain high-quality inference, they need to acquire comprehensive foundational datasets and immense computing power to train effective AI models. These resources are mainly 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, and also allow the top players to become stronger. They possess large foundational datasets and substantial 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, is there a way 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 primarily because a ready-made method to integrate these decentralized computing powers and operate them in a commercial way has not yet emerged. Here are the typical GPU utilization figures for different workloads:

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

• GPU utilization: 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 in the world of Web2, there are no effective measures to collect and integrate these resources. However, Crypto and the blockchain economy may be just the remedy for this challenge. The crypto economy constructs a highly efficient global market. Due to its unique token economy and characteristics of decentralization, the pricing, circulation, and matching of market supply and demand for resources are extremely efficient.

The development of AI affects the future of humanity, and the progress 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 centralized server purchases to computing power leasing, the barriers to obtaining computing power are gradually decreasing. Before the advent of cloud computing, companies had to procure servers themselves and continuously update them with technological advancements, 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 according to the amount used. Now, traditional enterprises are being disrupted by cloud computing. In the field of cloud computing, virtualization technology is the core of the field. Virtualized servers can divide 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; however, 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 traditional centralized systems being too far 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 speed of light limitation.

However, emerging industries such as the metaverse, autonomous driving, and telemedicine have extremely low latency requirements. Therefore, it is necessary to move 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 mining providers, the advantages of decentralized cloud computing mainly lie in:

• Accessibility and Flexibility: Accessing computing power chips from cloud service providers typically takes weeks, and high-performance GPU models, such as A100, H100, etc., are often out of stock. In addition, to obtain computing power, consumers usually need to sign long-term, inflexible contracts with these large companies, which not only results in time loss but also makes business operations rigid, losing a degree of flexibility. In contrast, decentralized computing power platforms allow access to computing power at any time and offer flexible hardware options, providing greater accessibility.
• Lower Prices: By utilizing idle chips and adding token subsidies from network protocol parties to chip and computing power suppliers, the distributed computing power network may offer cheaper computing power.
• Censorship Resistance: Some Web3 systems do not position themselves as permissionless systems. Compliance issues such as GDPR and HIPAA are addressed in the stages of GPU launch, data loading, data sharing, and result sharing.

With the further development of AI and the ongoing supply-demand imbalance of GPUs, more developers will be driven to turn to decentralized cloud computing platforms. At the same time, during a bull market, due to the rise in cryptocurrency prices, GPU suppliers will earn more profits, which will stimulate more GPU providers to enter this market, creating a positive flywheel effect.

Technical Challenges

1. Parallelization Problem

Distributed computing power platforms typically aggregate a long tail of chip supply, which means that individual chip suppliers can hardly complete complex AI model training or inference tasks independently 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 computing power of the platform.

However, the process of parallelization will face a series of issues, 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 the large capital investment in ASIC( dedicated integrated circuits) 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 trade-off, 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, can 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 it casually. Whether to use a decentralized platform or a centralized platform, price is just 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 attract these customers, leading to long-term partnerships and stable cash flow income.

Next, I will introduce the new distributed computing project Aethir, which focuses on game rendering and AI in this round of cycles, and calculate the possible valuation after listing based on the current AI projects and distributed computing projects in the same track on the market.

Aethir Project Introduction

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

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

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 for 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