Computing power leasing is essentially a B2B (business-to-business) commercial service. It's operated by professional cloud service providers like Jtti or dedicated high-performance computing (HPC) centers. They procure large-scale clusters of CPUs, GPUs, and even dedicated AI accelerator cards (such as NVIDIA A100/H100), divide them into standardized products using virtualization technology, and rent them out to users at fixed prices.

You can think of it as a "computing power hotel." The hotel (provider) has built standardized rooms (server instances) and equipped them with uniform facilities (software environment). As a tenant, you simply select the room type (instance specifications), the length of stay (rental period), and pay the fee online to obtain a clean, stable, and readily available room, without worrying about the building's construction or the utilities. The main forms include: public cloud on-demand instances (pay only for what you use, most flexible), reserved instances (commitment to rent for 1-3 years in exchange for significant discounts), and physical server hosting (renting an entire physical server, exclusive performance).

Computing power sharing is a collaborative model leaning more towards C2B or P2P (person-to-business or peer-to-peer). Its core idea is to connect globally distributed, idle computing devices (personal computers, mining rigs, surplus resources in enterprise data centers) through software, forming a massive, virtual "supercomputer." Resource contributors can receive tokens or cash rewards, while resource users can acquire computing power at a lower cost.

This is more like "computing power carpooling" or "computing power crowdfunding." A platform (such as the early SETI@home, or blockchain-based Render Network and Golem) coordinates the process. You, as a passenger (user), issue a computing task (such as rendering an animation). This task is broken down into numerous smaller packages and distributed to private cars (contributors) willing to hitch a ride and whose direction matches. Ultimately, once the task is completed, you pay the fee, and the contributors receive compensation.

Computing Power Leasing: The Main Force of Commercialization and Standardization

Computing power leasing is currently the mainstream in the market, supporting the vast majority of today's internet and digital economy.

Its core advantages lie in its stability, controllability, and ease of use. When you open an 8-GPU instance with A100 cards on Alibaba Cloud, you know its memory size, interconnect bandwidth, and expected performance, and you are almost guaranteed stable operation 24/7 during the rental period. The service provider is responsible for all hardware maintenance, network security, power supply, and basic cooling. What you get is a black-box service that is ready to use out of the box with guaranteed performance.

This is crucial for enterprise-level applications. A typical AI model training process might look like this, entirely built on leased computing power: Start a GPU instance pre-installed with PyTorch and CUDA on the cloud platform, pull the training dataset from object storage, and begin the training task:

python train.py --config config.yaml --gpus 8

After training, the model is automatically saved back to object storage, and the instance is released to save costs.

Throughout the entire process, the user does not need to touch the physical server at all. Key application scenarios include: artificial intelligence and deep learning (model training and inference), cloud rendering (movie and animation production), scientific computing and simulation (bioinformatics, fluid mechanics, financial modeling), and the backend of large websites and complex applications.

Computing Power Sharing: A New Exploration of Distributed and Decentralized Systems

The computing power sharing model is more idealistic and disruptive, attempting to solve the dual problems of computing power monopoly and resource idleness.

Its core appeal lies in its potential "cost advantage" and "resource utilization efficiency." Theoretically, it can mobilize massive amounts of idle resources (it is estimated that the global idle rate of computing power in personal devices is extremely high), converting sunk costs into usable computing power and providing it to the market at a price lower than commercial leasing. Simultaneously, based on blockchain incentive mechanisms, it can automatically complete task distribution, verification, settlement, and payment.

For example, in a decentralized rendering network, the process of an artist submitting a task might be abstracted as an interaction with a smart contract:

``solidity

// Conceptual smart contract code snippet (not actually executable)

contract DistributedRender {

struct Job {

address client;

string sceneFileIPFShash;

uint256 bidAmount;

bool completed;

}

mapping(uint256 => Job) public jobs;

function submitRenderJob(string memory _sceneHash, uint256 _bid) public payable {

// User submits a task and locks the bounty in the contract

uint256 jobId = generateJobId();

jobs[jobId] = Job(msg.sender, _sceneHash, _bid, false);

// Event triggered, rendering nodes in the network begin bidding for and calculating

}

function submitResult(uint256 _jobId, string memory _resultHash) public {

// Contributors submit results, and automatically receive token rewards after verification.

require(verifyResult(_resultHash), "Invalid result");

jobs[_jobId].completed = true;

payable(msg.sender).transfer(jobs[_jobId].bidAmount);

}

}

Main application explorations currently focus on: blockchain-related computing (its origin), graphics rendering (a typical use case of Render Network), life science research (such as Folding@home simulating protein folding through shared computing power), and niche or cutting-edge distributed computing experiments.

Comparison and Selection: How to Match the Right Model to Your Needs

Understanding the essence of the two models allows for comparison and decision-making from the following dimensions:

Ownership and Control of Resources:

Computing power leasing involves renting resources, with the service provider having absolute control. You cannot decide the upgrade pace of hardware models, but you receive standardized services and SLA (Service Level Agreement) guarantees.

Shared computing power involves using resources within a network where ownership is highly decentralized. You have little control over individual points of performance or stability, relying instead on the overall reliability and incentive mechanisms of the network.

Performance and Reliability:

Rentalized computing power provides deterministic, high-performance, and low-latency computing power. GPUs are interconnected at high speed via NVLink, and the network is either low-latency InfiniBand or high-speed Ethernet. Suitable for large-scale, tightly coupled parallel computing.

Shared computing power performance is uncertain and highly volatile. It is constrained by the home network bandwidth of contributors, the wide variety of equipment models, and the possibility of nodes going offline at any time. Suitable for highly parallel, fault-tolerant "coarse-grained" tasks.

Cost and Economic Model:

Rentalized computing power pricing is transparent but relatively fixed, following economies of scale; larger usage may result in greater discounts. Costs are predictable and are clearly defined operating expenses.

Shared computing power theoretically has the potential for lower costs, but prices can fluctuate dramatically due to market supply and demand (such as cryptocurrency prices). Payments are typically made using specific tokens, posing exchange rate risks.

Ease of Use and Ecosystem:

Computing power leasing is extremely mature and easy to use. It boasts a rich management console, monitoring tools, SDKs, and a large technical community support. It integrates seamlessly with mainstream development toolchains.

Computing power sharing is still in its early stages and has technical barriers to entry. It requires adaptation to specific distributed frameworks and handling complex issues such as task splitting and result verification. The community and toolchain are still under development.

So, how should you choose?

A simple decision-making framework is:

Choose computing power leasing if your needs are: enterprise-level production applications, require stable and high-performance guarantees, urgent tasks with strict completion time requirements, your team is familiar with mainstream cloud platforms, and you seek hassle-free and effortless operation and maintenance.

Consider exploring computing power sharing if your scenario is: individual developer or geek project, extremely limited budget, divisible computing tasks with high fault tolerance, not sensitive to completion time, and have a strong interest in decentralized concepts and the technology itself.

Computing power leasing and sharing are not simply a matter of one replacing the other, but rather two complementary paths in the process of computing power commoditization: "centralized supply" and "distributed collaboration." The former, with its business efficiency and reliability, has built a solid foundation for today's digital world; the latter, with its innovative technology and economic models, is exploring the future possibilities for more democratic and efficient use of computing resources. For the vast majority of commercial applications that seek certainty, computing power leasing is the only option at present; while for explorers who are willing to embrace uncertainty in exchange for new possibilities, the world of computing power sharing has just opened its doors.