Currently, mainstream cloud computing providers typically concentrate their computing power in relatively closed data centers consisting of hundreds of thousands of CPU-based servers to provide a constant stream of computing services to the global network. With the surge in market demand, such cloud computing service providers will further expand hardware, but the overall price level of computing power is still very expensive.

For example, the AI field requires huge computing support, which requires a large supply of computing power. Even GPU arithmetic hardware devices, the price is as high as several hundred thousand to a million ranging from some AI projects such as Alphago, which once defeated the Go master Lee Sedol, a training model will cost hundreds of thousands of dollars. The expensive arithmetic cost is also one of the elements that hinder the development of the AI segment.

Traditional centralized arithmetic platforms may only serve regional users due to trusted factors such as data security, making global expansion challenging. Similarly, large centralized computing power providers establish data centers in remote areas with fewer natural disasters. This makes it difficult for them to meet nearby computing demands in different regions, especially for high-computing requirements in applications like autonomous driving.

These two factors alone highlight that the current centralized arithmetic supply system not only struggles to support the development of the metaverse ecosystem but also serves as a significant obstacle to its progress and implementation. High-performance, cost-effective arithmetic resources will become a primary requirement for the development of the new digital era.

GPU is becoming the main tone of cloud computing power

When it comes to the hardware that generates computing power, the CPU stands as the primary hardware foundation for mainstream cloud computing service providers. While both CPUs and GPUs can generate computing power, CPUs are primarily utilized for complex logical calculations. On the other hand, GPUs, equipped with hundreds or thousands of cores, excel in large-scale parallel computations, making them more suitable for tasks like visual rendering and deep learning algorithms. In comparison, GPUs offer faster and more cost-effective computation, often at just one-tenth the cost of a CPU.

GPU computing power has become deeply integrated into various fields such as artificial intelligence, cloud gaming, autonomous driving, weather prediction, and space observation. These domains demand extensive high-end computational capabilities. With the concentrated growth of these high-end industries, the future market demand for GPU computing power is expected to far surpass that of CPU computing power. When it comes to building the metaverse, many graphics card manufacturers believe that GPUs play a pivotal role in determining the metaverse’s success as a virtual world and the immersive experience it offers to end-users.

AI leverages advanced data, tensor, and pipeline parallelization technologies, enabling efficient distribution of large-scale language model training across thousands of GPUs. This results in robust high-performance arithmetic support, a crucial component for the development of the metaverse.

Indeed, the model for supplying computational power needs a significant reshaping, and distributed collaboration holds the promise of enabling a rational supply and distribution of high-performance GPU computational power. Blockchain technology, known for its capacity to address trust and efficiency issues, establishes a distributed ecosystem where every network member can spontaneously and deeply engage, ultimately benefiting from it.

While blockchain technology has primarily found its footing in the financial sector, there’s the expectation that it will revolutionize the provision of distributed computational resources globally. This shift aims to replace the current centralized supply system, reducing computational costs, enhancing supply efficiency, and serving real-world industries.

As we enter the era of the metaverse and AI, the demand for affordable, high-performance computational resources has become increasingly urgent. Similarly, the GPU computational supply industry is poised to tap into this trillion-dollar market.

Coinda, serving as the primary distributed GPU computational power infrastructure, plays an exceptionally crucial role in this emerging digital era.

Coinda: Scalable GPU Cloud Computing Network

Coinda is a blockchain-based distributed GPU computing network that leverages GPU computing servers worldwide as its nodes. Theoretically, it offers unlimited scalability, allowing any computing power provider meeting the criteria to become a node and generate revenue. These nodes can automatically transform into metropolitan nodes and edge nodes, catering to nearby computing needs. Even if a node experiences a single point of failure, it doesn’t disrupt GPU computational supply, thanks to the system’s decentralization, which enhances fault tolerance.

Professional GPU computing power providers can deploy GPU servers in T3-level or higher IDC rooms for stability. They can then install Coinda software into these servers to access the Coinda computing power network. Some idle computational power can be harnessed through Coinda’s mining pool to boost GPU utilization rates and generate additional income. Consequently, Coinda gathers a substantial amount of distributed computational power, significantly reducing computational costs compared to centralized platforms and lowering the cost of GPU computational power acquisition.

As one of the few GPU distributed computational networks in the industry, Coinda has pioneered further tokenization of GPU computational power. Any Coinda network user holding computational tokens can cash them in through the network and freely trade these tokens via Coinda’s DEX. This new method of allocating computational power allows more users to participate in the digitalization process.

With Coinda’s main network set to launch in Q1 next year, anyone worldwide can freely contribute GPU resources meeting Coinda’s network requirements. Users can lease GPU resources within Coinda’s network to support their business development, with all transactions recorded on the blockchain, achieving complete decentralization.

Although Coinda’s model competes with mainstream cloud computing platforms, platforms like Ali Cloud and Amazon Cloud can access the Coinda network as computational nodes to generate revenue. Thus, Coinda’s relationship with these computational providers is competitive.

Currently, some AI research fields favor the services offered by the Coinda system. The Coinda team has consistently served the GPU computing power field, initially catering to over 500 universities worldwide with AI developer users. Coinda’s network has undergone targeted pilot programs, with many universities offering AI programs already using Coinda’s GPU computation. Application scenarios encompass cloud gaming, AI, autonomous driving, blockchain, visual rendering, and more, with over 20,000 AI developer users based on Coinda. To date, Coinda has facilitated the construction of over 50 GPU cloud platforms and served hundreds of enterprise customers.

It’s evident that with the convergence of 5G and AIOT, the global computing industry is entering the era of high-performance computation and edge intelligence. Coinda’s provision of real-time distributed high-performance and cost-effective GPU computation positions it as a critical computational infrastructure in the AI+5G era.