In-depth research report｜Features and challenges of distributed computing networks for heterogeneous equipment

Key points introduction

Why is "any device a computing power provider" still far away?

This report deeply explores the challenges facing the heterogeneous distributed computing network (DePIN) composed of PCs, mobile phones, edge devices, etc. from "technical feasibility" to "economic feasibility". From the voluntary calculation inspiration of BOINC and Folding@home to the commercialization attempts of DePIN projects such as Golem and Akash, the report sorts out the history, current situation and future of this track.

• Heterogeneous network problems : equipment performance differences, high network latency, and large node fluctuations. How to schedule tasks, verify results, and ensure security?
• Sufficient supply, scarce demand : it is easy to start coldly, and it is difficult to find real paid users. How can DePIN go from a miner game to a real business?
• Security and compliance : data privacy, cross-border compliance, responsibility ownership... Who will protect these hard issues that cannot be avoided by "decentralization"?

The report is about 20,000 words, and the estimated reading time is 15 minutes (This report is produced by DePINOne Labs. Please contact us for reprinting)

1. Introduction

**1.1 Definition of distributed computing network for heterogeneous

devices**

A distributed computing network refers to a network composed of geographically dispersed and diverse computing devices (such as personal computers, smartphones, IoT edge computing boxes, etc.), aiming to aggregate idle computing resources of these devices through the Internet connection to perform large-scale computing tasks.

The core idea of ​​this model is that modern computing devices usually have strong processing power, but are very low in utilization most of the time (for example, a regular desktop computer uses only 10–15% of the capacity). Distributed computing networks attempt to integrate these underutilized resources to form a huge virtual computing cluster.

Unlike traditional supercomputers (HPC) or centralized cloud computing, the most prominent feature of this type of distributed network is its heterogeneity .

Devices participating in the network have huge differences in hardware (CPU type, GPU model, memory size), operating system (Windows, macOS, Linux, Android), network connection quality (bandwidth, latency), and availability mode (the device may be online or offline at any time).

Managing and effectively utilizing this highly heterogeneous, dynamically changing resource pool is one of the core technical challenges facing such networks.

1.2 Historical background: Volunteer calculation

Despite many challenges, the technical feasibility of using distributed heterogeneous devices for large-scale computing has been fully demonstrated through decades of volunteer computing (VC) practice.

BOINC (Berkeley Open Infrastructure for Network Computing)

BOINC is a typical success story. It is an open source middleware platform that adopts a client/server architecture. Project parties run the server to distribute computing tasks, while volunteers run BOINC client software on their personal devices to perform these tasks. BOINC has successfully supported numerous scientific research projects, covering astronomy (such as SETI@home, Einstein@Home), biomedical (such as Rosetta@home), climate science and other fields, and has used volunteer computing resources to solve complex scientific problems. The BOINC platform has amazing computing power. Its aggregated computing power was several times that of the top supercomputers at that time , reaching the PetaFLOPS level, and this computing power came entirely from the idle personal computer resources contributed by volunteers. BOINC was designed to deal with network environments composed of heterogeneous, intermittently available and untrusted nodes. Although building a BOINC project requires some technical investment (about three-month workload, including system administrators, programmers, and web developers), its successful operation proves the technical potential of the VC model.

Folding@home (F@h)

F@h is another well-known volunteer computing project that has focused on helping scientists understand disease mechanisms and develop new therapies by simulating biomolecular dynamics processes such as protein folding, conformational changes, and drug design. F@h also uses volunteers’ personal computers (even in the early days, even the PlayStation 3 console) for large-scale parallel computing. The project has achieved remarkable scientific achievements, and has published more than 226 scientific papers, and its simulation results are well consistent with experimental data. Especially during the COVID-19 pandemic in 2020, public participation was in high enthusiasm, and the computing power of Folding@home aggregated reached the ExaFLOP level (10 billion floating-point operations per second) , becoming the world's first computing system to reach this scale, and strongly supporting the research of SARS-CoV-2 virus and the development of antiviral drugs.

Long-running projects like BOINC and Folding@home irrefutably prove that from a technical point of view, it is completely feasible to aggregate and utilize a large amount of distributed, heterogeneous, volunteer-provided equipment computing power to handle specific types of parallelizable, computationally intensive tasks (especially scientific computing) . They lay an important foundation for task distribution, client management, handling unreliable nodes, etc.

1.3 The rise of business models: Golem and DePIN computing

Based on the verification of technical feasibility of volunteer computing, projects have emerged in recent years that attempt to commercialize this model, especially the DePIN (Decentralized Physical Infrastructure Networks) computing project based on blockchain and token economy.

Golem Network was one of the early explorers in the field and was considered a pioneer in the DePIN concept. It builds a decentralized computing power market that allows users to purchase or sell computing resources (including CPU, GPU, memory and storage) through a peer-to-peer (P2P) approach. There are two main players in the Golem network: Requestors, i.e. users who need computing power; and providers, i.e. users who share idle resources in exchange for GLM tokens. Its target application scenarios include CGI rendering, artificial intelligence (AI) computing, cryptocurrency mining and other tasks that require a lot of computing power. Golem achieves scale and efficiency by splitting tasks into smaller subtasks and processing them in parallel on multiple provider nodes.

DePIN computing is a broader concept, referring to the use of blockchain technology and token incentive mechanisms to build and operate various physical infrastructure networks, including computing resources. In addition to Golem, there are also many other projects such as Akash Network (providing decentralized cloud computing services), Render Network (focusing on GPU rendering), io.net (aggregating GPU resources for AI/ML), and many other projects. The common goal of these DePIN computing projects is to challenge traditional centralized cloud computing service providers (such as AWS, Azure, GCP) to provide lower-cost and more flexible computing resources through decentralized methods. They try to use the token economic model to incentivize hardware owners around the world to contribute resources, thus forming a huge, on-demand computing power network.

From volunteer calculations relying primarily on altruism or community reputation (points) as incentives to DePIN adopting tokens for direct economic incentives, this represents a shift in the pattern. DePIN attempts to create economically sustainable and more general distributed computing networks, in a bid to go beyond specific areas such as scientific computing to serve broader market demand.

However, this shift also introduces new complexity, especially in terms of market mechanism design and stability of token economic models.

Preliminary assessment: Observation on oversupply and insufficient demand

The core dilemma facing the DePIN computing field at present is not to allow users to participate in the network to contribute computing power, but how to supply computing power to the network and provide services to various computing power needs .

• Supply is easy to guide : Token incentives are very effective in guiding suppliers to join the network.
• Demand is difficult to prove : it is much more difficult to generate real, paid demands. DePIN projects must provide competitive products or services that solve practical problems, not just rely on token incentives.
• Voluntary calculations prove technical feasibility, but DePIN must prove economic feasibility, depending on whether the demand-side problem can be effectively solved. Voluntary computing projects (such as BOINC, F@h) are successful because "demand" (scientific computing) has intrinsic value to the researchers running the project itself, while the supply side's motivation is altruism or interest.

DePIN builds a market where the supplier expects economic returns (tokens) while the demander must perceive the value of the service exceeds its cost. Using tokens to guide supply is relatively direct, but to create real payment needs, it is necessary to build services that can compete with or even surpass centralized services (such as AWS). Current evidence suggests that many DePIN projects still face huge challenges in the latter.

2. Core technical challenges of heterogeneous distributed networks

Building and operating a heterogeneous distributed computing network composed of mobile phones, personal computers, Internet of Things devices, etc. faces a series of severe technical challenges. These challenges stem from the physical dispersion of network nodes, the diversity of the devices themselves, and the unreliability of participants.

2.1 Equipment heterogeneity management

There are huge differences between devices in the network at the hardware level (CPU/GPU type, performance, architecture such as x86/ARM, available memory, storage space) and software level (operating systems such as Windows/Linux/macOS/Android and their versions, installed libraries and drivers). This heterogeneity makes it extremely difficult to deploy and run applications reliably and efficiently across the network. A task written for a specific high-performance GPU may not run on a low-end phone or be extremely inefficient.

BOINC 's response

BOINC handles heterogeneity by defining the “platform” (a combination of operating system and hardware architecture) and providing a specific “application version” for each platform. It also introduces a Plan Class mechanism that allows for finer task distribution based on more detailed hardware features such as specific GPU models or driver versions. Additionally, BOINC supports running existing executables using wrappers (Wrappers), or running applications in virtual machines (such as VirtualBox) and containers (such as Docker) to provide a unified environment across different hosts, but this brings additional performance overhead.

DePIN 's response

Many DePIN computing platforms also rely on containerization technologies (such as Akash using Docker) or specific runtime environments (such as Golem's gWASM, which may also support VM/Docker) to abstract differences between underlying hardware and operating systems and improve application compatibility. However, fundamental performance differences between devices remain. Therefore, the task scheduling system must be able to accurately match tasks to nodes with corresponding capabilities.

Device heterogeneity significantly increases the complexity of application development, deployment, task scheduling (matching tasks to the right node), performance prediction, and result verification. While virtualization and containerization provide a degree of solution, they cannot completely eliminate the differences in performance. To efficiently utilize the diverse hardware resources in the network (especially dedicated accelerators such as GPUs and TPUs), complex scheduling logic is required, and even different optimized application versions may be required for different types of hardware, which further adds complexity. Relying solely on general-purpose containers may result in the performance of dedicated hardware being inadequate.

2.2 Network latency and bandwidth limitations

Network latency refers to the time required for data to be transmitted between network nodes. It is mainly affected by physical distance (light speed limiting causes propagation delay), network congestion (causing queue delays), and device processing overhead. High latency can significantly reduce the system's response speed and throughput, affect user experience, and hinder the execution of tasks that require frequent interactions between nodes. In high bandwidth networks, latency often becomes a performance bottleneck.

Bandwidth refers to the maximum amount of data that a network connection can transmit within a unit time. Insufficient bandwidth can lead to network congestion, further increasing latency and reducing the actual data transmission rate (throughput). Volunteer computing and DePIN networks often rely on participants’ home or mobile Internet connections, which can have limited and unstable bandwidth (especially upload bandwidth).

High latency and low bandwidth greatly limit the types of workloads suitable for running on such networks . Tasks that require frequent communication between nodes, require a large amount of input/output data to be transmitted relative to the computational volume, or require real-time responses are often impractical or inefficient in this environment. Network limitations directly affect task scheduling strategies (data locality becomes critical, i.e., the calculations should be close to the data) and the transmission efficiency of the results. Especially for tasks such as AI model training that require a large amount of data transmission and synchronization, the bandwidth of consumer-level networks may become a serious bottleneck.

Network limitation is the result of the combined action of the laws of physics (delay is constrained by the speed of light) and economic factors (bandwidth cost). This makes distributed computing networks naturally more suitable for "Embarrassingly Parallel tasks that are computationally intensive, sparse communications, and easy to parallel. Compared with centralized data centers with high-speed internal networks, such network environments are often poor in communication efficiency and reliability, which fundamentally limits the scope of application and market size that they can effectively serve.

2.3 Node dynamics and reliability

Devices (nodes) participating in the network are highly dynamic and unreliable. Nodes may join or exit the network at any time (called "churn" or Churn), and devices may be powered off, disconnected from the network, or shut down by users. Furthermore, these nodes are generally untrusted and may return false results due to hardware failures (such as instability caused by overclocking) or malicious behavior.

This dynamicity makes task execution possible, resulting in wasted computing resources. Unreliable nodes affect the correctness of the final result. High churn rates make tasks that require long-running difficult to complete and cause difficulties in task scheduling. Therefore, the system's Fault Tolerance becomes crucial.

Generally speaking, there are several strategies for the instability of nodes:

• Redundancy/Replication : Assign the same task to multiple independent nodes to perform, and then compare their calculation results. It is accepted as valid only if the results are consistent (or within the allowable error range). This can effectively detect errors and malicious behavior and improve the reliability of the results, but at the cost of increasing the computational overhead. BOINC also adopts an adaptive replication strategy based on host historical reliability to reduce overhead.
• Checkpointing : allows applications to periodically save states in the middle. When the task is interrupted, execution can be resumed from the most recent checkpoint instead of starting from scratch. This greatly reduces the impact of node churn on task progress.
• Deadlines & Timeouts : Set a completion deadline for each task instance. If a node fails to return the result before the deadline, the instance is assumed to fail and the task is reassigned to other nodes. This ensures that the task can eventually be completed even if some nodes are unavailable.
• Work Buffering : The client downloads enough tasks in advance to ensure that the device can remain working and maximize resource utilization when the network connection is temporarily lost or new tasks cannot be obtained.

Handling unreliability is the core principle of distributed computing network design, not additional functions. Since nodes cannot be directly controlled and managed like in a centralized data center, the system must rely on statistical methods and redundant mechanisms to ensure the completion of tasks and the correctness of results. This inherent unreliability and its response mechanism increase the complexity and overhead of the system, thereby affecting the overall efficiency.

**2.4 Task management complexity: segmentation, scheduling and

verification**

Task segmentation : First, a large computing problem needs to be broken down into many small task units that can be executed independently. This requires that the problem itself has a high degree of parallelism, preferably a "easy parallelism" structure, i.e. there are few dependencies or communication requirements between subtasks.

Task Scheduling : Effectively allocating these task units to the appropriate nodes in the network for execution is one of the most core and challenging problems in distributed computing. In heterogeneous, dynamic network environments, task scheduling problems are usually proven to be NP-complete, meaning that there is no known polynomial time optimal solution. The scheduling algorithm must consider a variety of factors:

• Node heterogeneity : differences in node computing power (CPU/GPU), memory, storage, architecture, etc.
• Node dynamics : node availability, online/offline mode, churn rate.
• Network status : latency and bandwidth between nodes.
• Task characteristics : computational quantity, memory requirements, data quantity, dependency (if there is a dependency between tasks, it is usually expressed as directed acyclic graph DAG), and deadline.
• System policy : resource share allocation (such as BOINC's Resource Share), priority.
• Optimization goals : may include minimizing the total completion time (Makespan), minimizing the average task turnover time (Flowtime), maximizing throughput, minimizing costs, ensuring fairness, improving fault tolerance, etc. There may be conflicts between these goals.

The scheduling strategy can be static (allocated at one time before the task begins) or dynamic (adjusting the allocation according to the real-time state of the system, and divided into online mode and batch mode). Due to the complexity of the problem, heuristics, metaheuristics (Meta-heuristics, such as genetic algorithms, simulated annealing, ant colony optimization, etc.) and methods based on artificial intelligence (such as deep reinforcement learning) have been widely studied and applied. The BOINC client uses local scheduling policies (including work acquisition and CPU scheduling) to try to balance multiple goals such as deadline, resource share, and maximize points acquisition.

Result Verification : Since the node is untrusted, the correctness of the returned result must be verified.

• Replication-based verification : This is the most commonly used method, which is to let multiple nodes compute the same task and then compare the results. BOINC uses this method and provides "homogeneous redundancy" for tasks that require completely consistent results, ensuring that only nodes with the same software and hardware environment participate in the replication calculation of the same task. Golem also uses redundant verification and may adjust the verification frequency (probabilistic verification) based on the provider's reputation, or use Spot-checking. This method is simple and effective, but it is cost-effective (double in calculations or more).
• Nondeterminism problem : For some computing tasks, especially AI inference performed on GPUs, etc., there may be slight differences in outputs in different hardware or operating environments (computation nondeterminism) even if the input is the same. This invalidates the replication verification method based on precise result matching. New verification methods, such as comparing the semantic similarity of results (for AI output), or using statistical methods such as the SPEX protocol, to provide a guarantee of probability correctness.
• Cryptography method : Verifiable Computation technology provides a way to verify the correctness of the calculation without repeated execution.
• Zero-Knowledge Proofs (ZKPs) : allows the proofreader (computing node) to prove to the verifier that a certain calculation result is correct without revealing any input data or intermediate processes of the calculation. This is very promising for privacy protection and increased verification efficiency, but generating ZKP itself often requires a significant computational overhead, limiting its application in complex computing.
• Fully Homomorphic Encryption (FHE) : Allows arbitrary calculations directly on encrypted data to obtain the encryption results. After decryption, it is the same as the result calculated in plain text. This can achieve extremely high privacy protection, but the current FHE solution is extremely efficient and expensive, far from reaching the level of large-scale practicality.
• Trusted Execution Environments (TEEs) : Use hardware features (such as Intel SGX, AMD SEV) to create an isolated and protected memory area (enclave) to ensure the confidentiality and integrity of the code and data running there, and to provide proof to the remote party (remote proof). TEE provides a relatively efficient verification method, but it relies on specific hardware support, and its security also depends on the security of the hardware itself and the associated software stack.

Task management, especially scheduling and verification, is much more complex in heterogeneous, unreliable, untrusted distributed networks than in centralized cloud environments. Scheduling is a continuously active field of research (NP complete problem), while verification faces fundamental challenges such as nondeterminism, verification costs, and limits the types of computing tasks that can be performed and verified reliably and economically.

2.5 Cross-device security and privacy protection

Threat Environment : Distributed computing networks face security threats from multiple levels:

• Node level : Malicious nodes may return fake results or falsely report calculations to defraud rewards. The nodes controlled by the attacker may be used to run malicious code (if the project server is compromised, the attacker may attempt to distribute viruses masquerading as computing tasks). A node may attempt to access sensitive data from a host system or other nodes. Internal threats from volunteers or providers cannot be ignored either.
• Network level : Project servers can suffer from a Denial of Service (DoS) attack, such as being overwhelmed by a large amount of invalid data. Network communications may be eavesdropped (Packet Sniffing), resulting in the leakage of account information (such as keys, email addresses). An attacker may perform a man-in-the-Middle or IP spoofing.
• Project Level : Project parties may intentionally or unintentionally publish applications containing vulnerabilities or malicious features that harm participants’ devices or privacy. The input or output data files of the project may be stolen.
• Data Privacy : There is a privacy risk in processing data on untrusted nodes, especially when it comes to personally identifiable information (PII), commercially sensitive data or regulated data (such as medical information). Data may also be intercepted during transmission. Complying with GDPR, HIPAA and other data protection regulations is extremely challenging in distributed environments.

Mitigation mechanism :

• Results and Reputation Verification : Verify the correctness of the results through redundant calculations and detect malicious nodes. Establish a reputation system (such as Golem) to score and filter based on the historical behavior of nodes.
• Code Signing : The project party digitally signs the application it publishes. The client will verify the signature before running the task to ensure that the code has not been tampered with and prevent malicious code distribution (BOINC adopts this mechanism).
• Sandboxing and isolation : Run compute tasks in restricted environments (such as low-privileged user accounts, virtual machines, containers), preventing tasks from accessing sensitive files or resources on the host system. TEE provides strong hardware-based isolation.
• Server security : Take traditional server security measures, such as firewall, encrypted access protocol (SSH), disabling unnecessary services, and regular security audits. BOINC also provides upload certificates and size restriction mechanisms to prevent DoS attacks against data servers.
• Authentication and encryption : Use strong authentication methods (such as multi-factor authentication MFA, tokens, biometrics). Inter-node communication is encrypted using mTLS (such as Akash). Encrypt data in transit and at rest.
• Network security : Use network segmentation, zero-trust architecture, continuous monitoring and intrusion detection systems to protect network communications.
• Trusted Provider : Allows users to select providers audited and certified by trusted third parties (such as Akash's Audited Attributes).
• Privacy protection technology : Although expensive, technologies such as FHE and ZKP can theoretically provide stronger privacy protection.

Security is a multi-dimensional issue that requires the protection of the integrity and privacy of project servers, participant nodes, network communications, and the computing process itself. Despite the existence of various mechanisms such as code signature, redundant calculation, and sandboxing, the inherent untrustworthiness of participants requires the system designer to remain ongoing alert and accept the additional overhead caused. For commercial applications or scenarios involving sensitive data, how to ensure data privacy on untrusted nodes remains a huge challenge and a major barrier to adoption.

3. DePIN dilemma: matching computing power supply and demand

This section will deeply explore the difficulties in supply and demand matching, especially in workload allocation, service discovery, service quality assurance and market mechanism design.

3.1 Why is demand more difficult than supply?

In the DePIN model, it is relatively easy to use token incentives to attract suppliers (nodes) of computing resources to join the network. Many individuals and organizations have idle computing hardware (especially GPUs) that connect to the network in the hope of a return on tokens, which is often seen as a low-bar, low-friction approach to participation. The potential value of the token is enough to drive early growth on the supply side, forming the so-called "cold start".

However, the generation of requirements follows completely different logic and faces greater challenges. Just having a large amount of computing power supply does not mean that the network has economic value. Sustainable demand must come from users who are willing to pay to use this computing power. This means that the computing services provided by the DePIN platform must be attractive enough to solve the user's actual problems and be superior or at least not inferior to existing centralized solutions (such as AWS, GCP, Azure) in cost, performance, or specific features.

Token incentives themselves cannot create such real demand; they can only attract supply.

The current market situation also confirms this. Decentralized storage areas (such as Filecoin) have already experienced obvious problems of oversupply and low utilization, and their token economic activities are more focused on miners and speculation than meeting the storage needs of end users. In the field of computing, while scenarios such as AI and 3D rendering bring potentially huge demands, the DePIN platform still faces challenges in actually meeting these needs. For example, io.net aggregates a large number of GPUs, but the bandwidth and stability of consumer-grade GPUs may not be enough to support large-scale AI training, resulting in low actual utilization. Although Render Network benefits from OTOY's user base, its token burn rate is much lower than the issuance rate, indicating that the actual application is still insufficient.

Therefore, the DePIN model naturally tends to promote supply through tokenization. However, the generation of demand requires going through the traditional "product-market matching" process, overcoming strong market inertia and competing with mature centralized service providers, which is essentially a more difficult business challenge. This asymmetry in the supply and demand generation mechanism is the core economic dilemma currently facing the DePIN computing model.

3.2 Challenges in workload allocation and service discovery

In a DePIN computing network, effectively allocating user's computing tasks (demands) to appropriate computing resources (provideds) in the network is a complex process involving service discovery and workload matching.

Matching complexity : Demands often have very specific requirements, such as the requirement for a specific model of GPU, the minimum number of CPU cores, memory size, storage capacity, a specific geographic location (to reduce latency or meet data sovereignty requirements), and even specific security or compliance certifications. The resources provided by the supplier are highly heterogeneous. It is a difficult task to accurately match every requirement to a cost-effective provider that meets all conditions in a huge and dynamically changing supply pool.

Service Discovery Mechanism : How do users find providers that meet their needs? DePIN platforms usually adopt market-oriented methods to solve service discovery problems:

• Marketplace/Order Book : The platform provides a marketplace where the provider publishes its resources and quotes, and the demanders publishes their needs and the prices they are willing to pay. For example, Akash Network adopts this model and combines the reverse auction mechanism.
• Task Templates & Registry : The Golem network allows demanders to use predefined or customized task templates to describe calculation requirements and to apply the registry to find providers who can perform these template tasks.
• Auction Mechanisms : Akash's reverse auction (demand sets the highest price, provider bids) is a typical example, aiming to lower prices through competition.

Pricing mechanism : Prices are usually determined by market supply and demand dynamics, but may also be affected by factors such as provider reputation, resource performance, service level, etc.26. For example, Render Network adopts a multi-layer pricing strategy that takes into account speed, cost, security, and node reputation.

Current limitations

The existing matching mechanism may not be optimal. It is not enough to find "available" resources, the key is to find "suitable" resources. As mentioned earlier, consumer hardware may not be able to perform AI training tasks due to insufficient bandwidth, even if its GPU computing power itself is sufficient. Finding a provider that meets a specific compliance (such as HIPAA) or security standards can also be difficult because the provider backgrounds in the DePIN network vary.

Effective load allocation requires far more than simple resource availability checks. It requires complex discovery, matching and pricing mechanisms that accurately reflect the capabilities, reliability and specific requirements of the demander. These mechanisms are still constantly evolving and improving in the current DePIN platform. If the matching process is inefficient or the results are poor (for example, assigning tasks with high bandwidth requirements to low bandwidth nodes), the user experience will be greatly reduced and the value proposition of DePIN is weakened.

3.3 Problems in quality of service (QoS) guarantee

In traditional centralized cloud computing, service providers usually promise certain service quality through service level agreements (SLAs), such as ensuring specific uptime, performance metrics, etc. Although the execution of these SLAs may sometimes be biased towards providers, they at least provide a formal framework for quality expectations.

In a DePIN network consisting of a large number of unreliable, uncontrolled nodes, it is much more difficult to provide similar QoS guarantees.

• Lack of centralized control : No single entity can fully control and manage the performance and reliability of all nodes.
• Difficulty in verifying off-chain events : The blockchain itself cannot directly observe and verify real-world events that occur off-chain, such as whether a computing node has actually achieved the promised computing speed, or whether its network connection is stable. This makes automated QoS execution based on blockchain difficult.
• Individual default risk : In a decentralized market, any participant (provider or demander) may violate the agreement. The provider may not be able to provide the promised QoS, and the demander may refuse to pay.

In order to build trust in a decentralized environment and try to safeguard QoS, some mechanisms have emerged:

• Witness Mechanisms : Introduce independent third-party “witnesses” (usually motivated community members) to monitor off-chain service quality and report to the network in case of SLA violations. The effectiveness of this mechanism relies on reasonable incentive design to ensure that witnesses perform their duties honestly.
• Reputation Systems : Establish reputation scores by tracking the provider's historical performance (such as task success rate, response time, reliability). Demandators can choose providers based on reputation, and providers with poor reputation will have difficulty getting the task. This is one of the key mechanisms Golem adopts.
• Audited Providers : Rely on trusted auditors to review and authenticate providers' hardware, security standards and operational capabilities. Demandators can choose to use only providers passing audits, thereby improving the credibility of service quality. Akash Network is implementing this mode.
• Pledge and Punishment (Staking/Slashing) : Providers are required to pledge a certain amount of tokens as margin. If the provider acts inappropriately (such as providing false resources, failing to complete tasks, malicious behavior) or failing to meet certain service standards, the tokens they stake will be "slashed" (Slash). This provides financial constraints for providers to be honest and trustworthy.

Overall, QoS guarantees in DePIN networks are generally weaker and less standardized than traditional cloud SLAs. Currently, it relies more on the provider’s reputation, audit results or basic redundancy mechanisms than strict, enforceable contractual guarantees.

The lack of strong and easy-to-execute QoS guarantees is a major obstacle to the adoption of DePIN for enterprise-level users and business-critical applications. How to establish reliable service quality expectations and trust without centralized control is a key issue that DePIN must solve when it matures. Centralized cloud implements SLA by controlling hardware and networks, while DePIN needs to rely on indirect, economic incentives and community supervision mechanisms, and the reliability of these mechanisms remains to be tested by the market for a long time.

3.4 Market mechanism: pricing, reputation and provider selection

An effective market mechanism is the key to the DePIN platform's successful matching of supply and demand and building trust.

DePINs are often market-driven pricing, aiming to provide lower costs than centralized cloud fixed prices through competition. Common pricing mechanisms include:

• Auction/Order Book : For example, in reverse auction of Akash, the demander sets a price limit and the provider bids.
• Negotiable pricing : For example, Golem allows providers and demanders to negotiate prices to a certain extent.
• Layered pricing : For example, Render provides different price levels based on factors such as speed, cost, safety, reputation, etc. The price discovery process may be complex and requires balancing the interests of both supply and demand.

Reputation is an integral part of building trust in a decentralized market filled with anonymous or pseudonymous players. The Golem network uses an internal reputation system to rate providers and demanders based on factors such as task completion, payment timeliness, and correct results. Reputation systems help identify and exclude malicious or unreliable nodes.

Users need effective tools to filter and select reliable providers that meet their needs. Golem 主要依赖声誉评分来帮助用户过滤提供者； Akash Network 引入了“经审计属性”（Audited Attributes）的概念。用户可以在其部署描述语言（SDL）文件中指定，只接受那些通过了可信实体（如Akash 核心团队或其他未来可能的审计方）审计的提供者的投标。此外，社区也在讨论引入用户评价系统（Tier 1）和集成更广泛的第三方审计（Tier 2）。 Akash 还通过提供者激励计划（Provider Incentives Program）吸引高质量、承诺提供长期服务的专业提供者加入网络。

声誉系统面临的最大挑战是可能出现被操纵（刷分）情况。审计机制的有效性取决于审计方的可信度和审计标准的严格性。确保网络中有足够数量和种类的、高质量的提供者，并且这些提供者能够被需求者方便地发现，仍然是一个持续的挑战。例如，尽管Akash 网络上A100 GPU 的利用率很高，但其绝对数量仍然短缺，难以满足所有需求。

有效的市场机制对于DePIN 的成功至关重要。虽然拍卖等机制有助于价格竞争，但声誉和审计系统是控制质量和降低风险的关键补充层。这些机制的成熟度、可靠性和抗操纵性，直接影响用户对平台的信心和采纳意愿。如果用户无法通过这些机制可靠地找到满足需求的优质提供者，那么DePIN 市场的效率和吸引力将大打折扣。

4. 经济可行性：激励与代币经济学

DePIN 的核心创新之一在于其试图通过代币经济学（Tokenomics）来解决分布式基础设施建设和运营中的激励问题。本节将探讨从志愿计算到DePIN 的激励机制演变、计算网络代币经济模型的设计挑战，以及如何在贡献者奖励和消费者价值之间取得平衡。

4.1 激励机制的演变：从BOINC 积分到DePIN 代币

志愿计算项目如BOINC 主要依赖非经济激励。BOINC 建立了一套“积分”（Credit）系统，根据参与者完成的计算量（通常基于FLOPS 或基准测试的CPU 时间）来量化其贡献。这些积分的主要作用是提供声誉、满足参与者的竞争心理（例如通过团队排名）以及在社区内获得认可。积分本身通常不具有直接的货币价值，也不能交易。BOINC 的积分系统设计目标是公平、难以伪造，并支持跨项目积分追踪（通过第三方网站实现）。

DePIN 项目则将加密代币（如Golem 的GLM、Akash 的AKT、Render 的RNDR/RENDER、Helium 的HNT、Filecoin 的FIL 等）作为其核心激励机制。这些代币通常具有多种功能：

• 交换媒介 ：作为平台内购买服务（如计算、存储、带宽）的支付手段。
• 激励 ：奖励那些贡献资源（如计算能力、存储空间、网络覆盖）的参与者，是引导供应侧（Supply-side Bootstrapping）的关键工具。
• 治理 ：代币持有者通常可以参与网络的决策过程，如投票决定协议升级、参数调整、资金使用等。
• 质押（Staking） ：用于保障网络安全（例如，Akash 的验证节点需要质押AKT），或者可能作为提供或访问服务的条件。

从BOINC 的非金融、基于声誉的积分系统，到DePIN 的直接金融、基于代币的激励系统，这是一个根本性的转变。DePIN 旨在通过提供直接的经济回报来吸引更广泛、更具商业动机的资源供应者。然而，这也引入了加密货币市场波动性、代币估值、经济模型可持续性等一系列新的复杂问题。代币奖励的价值不再是稳定的积分，而是与市场价格挂钩，这使得激励效果变得不稳定，并给设计可持续的经济循环带来了挑战。

4.2 为计算网络设计可持续的代币经济模型

理想的DePIN 代币经济模型旨在创建一个正向循环，即“飞轮效应”（Flywheel Effect）。其逻辑是： 代币激励吸引资源供应→ 形成的资源网络提供服务→ 有价值的服务吸引付费用户（需求）→ 用户支付（或消耗代币）增加了代币的价值或效用→ 代币价值提升或效用增强进一步激励供应方加入或留存→ 供应增加提升网络能力，吸引更多需求 。

Core Challenge

• 平衡供需激励 ：如何在奖励供应方（通常通过代币增发/释放，即通胀）和驱动需求方（通过代币销毁/锁定/使用，即通缩或效用）之间找到平衡点，是设计的核心难点。许多项目面临高通胀率和需求侧代币消耗不足的问题，导致代币价值难以维持。
• 奖励与价值创造挂钩 ：激励机制应尽可能与真实的、对网络有价值的贡献（如成功完成计算任务、提供可靠的服务）挂钩，而不仅仅是简单的参与或在线时长。
• 长期可持续性 ：随着早期代币释放的减少或市场环境的变化，模型需要能够持续激励参与者，避免因激励不足导致网络萎缩。
• 管理价格波动 ：代币价格的剧烈波动会直接影响提供者的收入预期和需求者的使用成本，给经济模型的稳定性带来巨大挑战。Akash Network 引入USDC 支付选项，部分原因就是为了解决这个问题。

模型实例

• Golem (GLM) ：主要定位为支付代币，用于结算计算服务费用。其价值与网络的使用量直接相关。项目从GNT 迁移到了ERC-20 标准的GLM 代币。
• Render Network (RNDR/RENDER) ：采用“燃烧与铸造均衡”（Burn-and-Mint Equilibrium, BME）模型。需求者（渲染任务提交者）燃烧RENDER 代币来支付服务费，而提供者（GPU 节点运营者）则通过铸造新的RENDER 代币获得奖励。理论上，如果需求（燃烧量）足够大，超过了奖励的铸造量，RENDER 可能成为通缩型代币。该项目已将其代币从以太坊迁移到Solana。
• Akash Network (AKT) ：AKT 代币主要用于网络安全（验证节点质押）、治理投票，并且是网络内的默认结算货币（尽管现在也支持USDC）。网络从每笔成功的租约中收取一部分费用（Take Fee），用于奖励AKT 质押者。AKT 2.0 升级旨在进一步优化其代币经济学。

DePIN 代币经济学仍处于高度实验性的阶段。要找到一个既能有效启动网络、又能持续激励参与、并将激励与真实经济活动紧密结合的模型，是极其困难的。许多现有的模型似乎面临通胀压力，或者过度依赖市场投机而非内生价值。如果代币的发行速度远超因实际使用而产生的消耗或购买压力，代币价格就可能下跌。价格下跌会降低对提供者的激励，可能导致供应萎缩。因此，将代币价值与网络服务的实际使用量（需求）强力挂钩，对于DePIN 的长期生存至关重要。

4.3 平衡贡献者奖励与消费者价值主张

DePIN 平台必须在两个方面取得微妙的平衡：

1. 对供应方的奖励 ：奖励（主要是代币）必须足够有吸引力，能够激励足够数量且高质量的提供者加入并持续运营他们的计算资源。
2. 对需求方的价值 ：提供给消费者（计算任务需求者）的价格必须显著低于、或者在性能/功能上优于中心化的替代方案（如AWS、GCP），才能有效吸引需求。

DePIN 项目声称其“轻资产”（Asset-lite）模式（协议开发者不直接拥有硬件）和利用未充分利用资源的能力，使其能够以更低的运营成本提供服务，从而在奖励提供者的同时，也能为消费者提供更低的价格。特别是对于那些硬件已经折旧或运营成本较低的提供者（如使用消费级硬件），其期望的回报率可能低于大型数据中心。

维持供需平衡面临的挑战

• 代币波动性 ：代币价格的不稳定使得这个平衡难以维持。如果代币价格大幅下跌，提供者的实际收入会减少，可能导致其退出网络，除非提高服务价格（以代币计价），但这又会削弱对消费者的吸引力。
• 服务质量与价格匹配 ：消费者支付的价格不仅要低，还需要获得与之相匹配的、可靠的服务质量（QoS）。确保提供者能够持续提供满足需求的性能和稳定性，是维持价值主张的关键。
• 竞争压力 ：DePIN 项目之间的竞争可能导致在奖励方面“竞相杀价”（Race to the bottom），提供不可持续的高额奖励来吸引早期用户，但这会损害长期经济健康。

DePIN 的经济可行性取决于能否找到一个可持续的平衡点：在这个平衡点上，提供者能够获得足够的收入（考虑到硬件、电力、时间和代币价值波动等成本），而消费者支付的价格显著低于云巨头，并获得可接受的服务。这个平衡窗口可能相当狭窄，并且对市场情绪和代币价格非常敏感。提供者有实际的运营成本，代币奖励必须能够覆盖这些成本并提供利润，同时还要考虑代币本身的价值风险。消费者则会将DePIN 的价格和性能与AWS/GCP 进行直接比较。DePIN 必须在某个维度（主要是成本）上展现出巨大优势才能赢得需求。网络内部的费用机制（如交易费、租约费）或代币燃烧机制，必须能够在为提供者提供足够奖励的同时，保持对消费者的价格竞争力。这是一个复杂的优化问题，尤其是在加密资产价格剧烈波动的背景下。

5. 法律与监管的影响

DePIN 项目，特别是涉及跨国界的分布式计算网络，在运营中不可避免地会遇到复杂的法律和监管问题。这些问题涉及数据主权、隐私法规、跨境数据流动、代币定性以及去中心化治理的责任归属等多个方面。

5.1 数据主权、隐私法规与跨境数据流

数据主权（Data Sovereignty） ：许多国家制定了法律，要求特定类型的数据（尤其是敏感数据或公民个人数据）必须存储或处理在本国境内。DePIN 网络天然是全球分布的，计算任务和数据可能在不同国家的节点之间流转，这很容易与各国的数据主权法规产生冲突。

隐私法规（Privacy Regulations） ：如欧盟的《通用数据保护条例》（GDPR）等法规对个人数据的收集、处理、存储和传输制定了极其严格的规则。DePIN 网络如果处理涉及个人身份信息（PII）或用户行为的数据（例如，某些计算任务的输入或输出可能包含此类信息），就必须遵守这些法规。GDPR 具有域外效力，即使DePIN 平台或节点位于欧盟之外，只要其服务对象或监控行为涉及欧盟居民，也需遵守GDPR。在一个由大量匿名或假名节点组成的分布式网络中，确保所有节点都符合GDPR 等法规的要求，是一项巨大的挑战。

跨境数据流（Cross-Border Data Flows） ：将数据从一个司法管辖区传输到另一个司法管辖区受到严格的法律限制。例如，GDPR 要求数据接收国必须提供与欧盟“实质等同”的数据保护水平（即“充分性认定”），否则必须采取额外的保障措施，如标准合同条款（SCCs）并进行影响评估。美国的《澄清合法海外使用数据法案》（CLOUD Act）允许美国执法机构要求总部在美国的服务提供商提供存储在全球任何地方的数据，这进一步加剧了国际数据传输的法律冲突。DePIN 计算任务的输入数据分发和结果数据回收，几乎不可避免地涉及跨境数据流动，使得合规变得异常复杂。

这些法律要求与DePIN 的去中心化、无国界特性形成了直接的矛盾。确保合规可能需要复杂的技术解决方案，例如根据数据类型和来源地对任务进行地理围栏（Geofencing）或过滤，但这会增加系统的复杂性并可能限制网络的效率和规模。合规性问题是DePIN 处理敏感数据或在受严格监管行业（如金融、医疗）应用的主要障碍。

5.2 去中心化系统中的责任与问责

在传统的中心化服务中，责任主体通常是明确的（即服务提供商）。但在一个由众多独立、甚至匿名的参与者组成的去中心化网络中，当出现问题时，确定由谁承担法律责任变得非常困难。 For example:

• 如果一个计算节点返回了错误的结果，导致用户遭受经济损失，谁应负责？是节点提供者、协议开发者、还是用户自己承担风险？
• 如果一个提供者节点被黑客入侵，导致用户数据泄露，责任归属如何界定？
• 如果网络被用于非法活动（如运行恶意软件、进行非法内容的计算），谁来承担法律责任？

责任归属不清不仅使用户难以追索损失，也让提供者和开发者面临潜在的法律风险。如何处理用户与提供者之间的纠纷？如何确保提供者遵守当地的法律法规（例如内容过滤要求）？

目前的DePIN 项目主要依靠代码层面的机制（如智能合约自动执行支付）、声誉系统（惩罚不良行为者）以及可能的链上或链下仲裁机制（尽管相关细节在提供的材料中不明确）来处理纠纷和规范行为。然而，这些机制在法律上的效力往往是未经检验的。

缺乏明确的法律框架来界定去中心化系统中的责任，给所有参与者（用户、提供者、开发者）都带来了法律上的不确定性和风险。这种不确定性是阻碍DePIN 被主流企业采用的重要因素之一。如何在去中心化的前提下建立有效的问责机制，是DePIN 面临的一个重大的法律和技术挑战。中心化服务商（如AWS）因其明确的责任主体而更容易被企业信任，而DePIN 的分布式结构使得法律责任的分配和执行变得模糊不清，从而增加了商业应用的风险。

5.3 DePIN 代币与网络治理的不明确状态

DePIN 项目发行的代币在法律上应如何定性（是证券、商品还是实用型代币）？

这是一个在全球范围内都悬而未决的问题，尤其是在美国证券交易委员会（SEC）等监管机构采取强硬立场的情况下。监管机构缺乏清晰、前瞻性的指导方针，导致项目方和投资者都面临巨大的法律不确定性。如果代币被认定为未注册的证券，项目方、开发者甚至代币持有者都可能面临严厉的处罚。这种模糊状态严重阻碍了DePIN 项目的融资、规划和发展。

治理（Governance） ：许多DePIN 项目采用去中心化治理模式，允许代币持有者通过投票等方式参与网络规则的制定、协议的升级和社区资金的管理。然而，这种去中心化治理结构的法律地位和责任界定同样不明确。这些治理决策在法律上有多大约束力？如果治理决策导致网络出现问题或损害了某些参与者的利益，谁来承担责任？是投票的代币持有者、核心开发团队，还是协议本身

监管滞后（Regulatory Lag） ：技术创新的速度往往远超监管政策的更新速度。监管机构常常在缺乏明确规则的情况下，采取“执法即监管”（Regulation by enforcement）的方式，对已有的项目进行处罚，这给整个行业带来了寒蝉效应，扼杀了创新。

监管上的模糊性，特别是围绕代币分类和治理责任的问题，是笼罩在整个DePIN 行业上空的阴云。行业迫切需要监管机构提供更清晰、更适应技术发展的规则，以便项目能够将资源投入到技术和产品开发，而不是法律合规的猜测和应对中。这种法律上的迷雾使得企业在决定是否采用或投资DePIN 技术时犹豫不决。

6. 用户体验

尽管DePIN 计算网络在理论上具有成本和去中心化等优势，但其用户体验（UX） — — 无论是对于贡献资源的提供者还是使用资源的消费者— — 往往是采用的主要障碍。与成熟的中心化云平台相比，参与DePIN 网络通常需要更高的技术门槛和更复杂的操作流程。

6.1 加入和管理节点：贡献者（提供者）视角

BOINC 志愿者的体验 ：BOINC 的设计目标之一是让普通公众能够轻松参与，因此其客户端软件力求简单易用。志愿者只需下载安装客户端程序，选择感兴趣的科学领域或具体项目，之后客户端会在后台自动下载并运行计算任务，对用户日常使用电脑的影响很小。这个过程相对简单，技术门槛较低。然而，对于运行BOINC 项目的研究人员来说，设置项目服务器、移植应用程序到各种平台、编写任务提交脚本等工作可能相当复杂。引入虚拟机技术虽然缓解了应用移植的难题，但也增加了配置的复杂性。

Golem 提供者的体验 ：成为Golem 网络的提供者需要安装特定的提供者代理软件（提供了Linux 安装包）。用户需要配置愿意分享的资源（CPU、内存、磁盘等）。这通常需要一定的Linux 系统操作知识。此外，提供者需要理解和管理GLM 代币的接收和钱包操作。

Akash Network 提供者的体验 ：Akash 的提供者通常是数据中心运营商或拥有服务器资源的个人/组织。他们需要设置物理或虚拟服务器，并运行Akash 的提供者守护进程（Provider Daemon）来接入网络。这通常需要较高的技术能力，例如熟悉Linux 服务器管理、网络配置，并且常常隐含着对Kubernetes 等容器编排技术的了解，因为Akash 主要运行容器化工作负载。提供者还需要管理AKT 代币（用于接收奖励或潜在的质押）、参与市场竞标，并可能需要通过审计流程以获得可信认证。某些特定的DePIN 平台可能还有硬件要求，例如P2P Cloud 的TEE 功能需要AMD EPYC 处理器。

DePIN 普遍情况 ：不同DePIN 项目的提供者设置复杂度差异很大。一些项目（如Helium 的无线热点）力求“即插即用”的体验，但计算类DePIN 通常要求提供者具备更高的技术素养。管理加密货币钱包和处理代币交易对非加密货币用户来说是一个额外的学习曲线和操作障碍。

相比于BOINC 面向广大志愿者的易用性设计，商业化的DePIN 计算平台对提供者的技术要求普遍更高。提供者需要像运营小型业务一样管理其节点、资源、定价和收款。这限制了潜在提供者的范围，使其更偏向于专业的技术人员或机构，而非普通电脑用户。

6.2 访问和利用资源：消费者（需求者）视角

BOINC 的“消费者” ：BOINC 主要是为需要大规模计算的 研究项目 设计的。研究人员需要建立和维护项目服务器，管理应用程序、工作单元的生成和分发，以及结果的收集和验证。它并非面向需要按需获取通用计算能力的普通消费者或开发者。

Golem 需求者的体验 ：需求者需要通过Golem 提供的API 或SDK（如JS API、Ray 接口）来定义和提交计算任务。这通常需要使用任务模板（可以使用预制的或自定义创建）来描述任务逻辑、资源需求和验证方法。需求者需要持有并使用GLM 代币进行支付。他们还需要利用声誉系统来帮助选择可靠的提供者。这整个过程需要一定的编程能力和对Golem 平台的理解。

Akash Network 需求者的体验 ：Akash 的用户（租户）需要使用其特定的“堆栈定义语言”（Stack Definition Language, SDL）来描述他们需要部署的应用容器、资源需求（CPU、内存、存储、GPU）、持久化存储、网络设置以及对提供者的要求（如地理位置、审计认证等）。然后将此SDL 文件提交到市场进行反向拍卖，选择合适的提供者出价并创建租约。支付可以使用AKT 或USDC。这个过程要求用户熟悉容器化（Docker）概念，最好也了解Kubernetes 的基本原理。虽然Akash 提供了命令行工具和一些图形化界面来简化操作，但其底层逻辑和操作流程对于习惯了AWS、Azure、GCP 等云平台控制台和API 的用户来说，仍然有较大的学习成本。

DePIN 普遍情况 ：使用DePIN 计算资源通常比使用传统云服务更复杂。用户往往需要与区块链钱包、代币进行交互，理解去中心化概念（如租约、提供者信誉），并学习平台特定的工具和语言（如SDL、任务模板、SDK）。与成熟云服务商提供的丰富且熟悉的工具链（如监控、日志、调试、集成服务）相比，DePIN 平台的配套工具通常还不够完善，这增加了开发和运维的难度。

对于终端用户（需求者）而言，DePIN 计算平台的学习曲线通常比主流云平台更陡峭。它不仅要求用户具备相应的技术能力（如容器化、特定API 使用），还需要用户理解和操作加密货币相关的流程。 这种复杂性是阻碍DePIN 被广泛采用的一个重要因素。

6.3 易用性比较：BOINC vs. Golem vs. Akash

• BOINC ：对 贡献者 （志愿者）来说最简单，安装后基本无需干预。对 消费者 （研究项目方）来说则非常复杂，需要自行搭建和运营整个项目后端。
• Golem ：试图通过API 和市场为供需双方提供接口。对提供者和需求者都需要一定的技术知识，并且需要处理加密货币。早期更侧重于特定用例（如渲染），逐步扩展到更通用的计算（如gWASM）。
• Akash Network ：目标用户更接近于熟悉容器/Kubernetes 的云开发者。SDL 提供了强大的部署灵活性。提供者设置技术要求高，需求者也需要学习SDL 和处理加密货币。相比Golem，Akash 的目标是支持更广泛的云原生工作负载。虽然提供了用户界面，但底层复杂性依然存在。

用户体验因目标用户（贡献者vs 消费者）和平台定位（科学研究vs 商业市场vs 云替代品）而异。目前，没有任何一个平台在通用计算服务的易用性上能够与主流云服务商匹敌。对于已经熟悉容器技术的开发者来说，Akash 可能提供了相对更平滑的过渡路径，但对于更广泛的用户群体，DePIN 的使用门槛仍然较高。

7. 分布式计算vs. 中心化云

将基于异构设备的分布式计算网络（包括志愿计算和DePIN 模式）与传统的中心化云计算（以AWS、Azure、GCP 为代表）进行比较，可以揭示两者在架构、成本、性能、可靠性、可扩展性和适用场景上的显著差异。

7.1 架构与资源管理模型

• 中心化云 ：采用 集中式架构 ，计算、存储、网络等资源集中部署在由云服务提供商拥有和管理的大型数据中心内。提供商负责所有底层基础设施的管理和维护，并通过虚拟化技术将资源池化，向多个客户提供服务。用户通过控制台或API 按需获取服务，通常无需关心底层硬件细节，享有高度的 抽象 和 自服务 能力。
• 分布式计算网络 ：采用 去中心化架构 ，资源分布在地理上分散的、由众多独立参与者（志愿者或提供者）拥有的设备上。资源的管理和协调需要通过协议在节点间进行，通常由用户或协议本身承担更多管理责任， 抽象程度较低 ，但可能提供 更大的控制权 。

7.2 成本结构与经济效率

• 中心化云 ：通常采用 按需付费（Pay-as-you-go）的运营支出（OpEx）模式。虽然避免了用户的前期硬件投入，但大规模或长期使用成本可能很高。存在供应商锁定（Vendor Lock-in）的风险。云服务商自身需要承担巨大的资本支出（CapEx）来建设和维护数据中心。市场由少数几家巨头主导，可能形成价格垄断 。提供商会为预留实例或长期承诺提供折扣。

分布式计算网络 ：

• 志愿计算（BOINC） ：对研究人员来说成本极低，主要开销是服务器和少量管理人员的费用。计算资源本身是免费的。
• DePIN 计算 ：核心价值主张是 显著降低成本 。这得益于其 轻资产模式 （协议开发者不拥有硬件）和利用 未充分利用的资源 （这些资源的边际成本或机会成本可能很低）。通过 市场竞争 （如拍卖）进一步压低价格。对提供者而言， 进入门槛较低 ，可以利用现有硬件创收。但DePIN 模式的成本也可能受到 代币价格波动 的影响。对提供者来说，可能需要前期硬件投入（如果专门购买设备参与）

7.3 性能、延迟与适用工作负载

• 中心化云 ：通常能为各种类型的工作负载提供良好且相对 可预测的性能 。数据中心内部采用 高速、低延迟的网络 连接，非常适合需要节点间频繁通信或对延迟敏感的应用。能够提供各种规格的实例，包括 顶级的、专用的硬件 （如最新的GPU、TPU）。
• 分布式计算网络 ： 性能差异巨大且不稳定 ，取决于参与节点的硬件能力和当时的网络状况。由于依赖公共互联网且节点地理分散， 网络延迟通常很高且变化不定 。最适合运行 可大规模并行、计算密集、对延迟不敏感 的任务（即高吞吐量计算HTC）。不适合交互式应用、数据库、需要低延迟响应或节点间紧密协作的任务。其优势在于能够聚合 极大规模的并行计算能力 。DePIN 平台探索的“异构集群”（Heterogeneous Clustering）可能成为一个独特的性能优势，即将不同类型的GPU 组合起来完成大型任务。

7.4 可靠性、容错性与安全态势

• 中心化云 ：通过在数据中心内部署冗余硬件和网络连接来提供 高可靠性 ，并通常提供 SLA 保证。但仍然存在 中心化故障点 的风险（例如，整个区域的服务中断）。安全由云服务商负责，他们投入巨资进行防护，但用户数据最终存储和处理在第三方控制的环境中。
• 分布式计算网络 ：去中心化的特性使其具有 潜在的高容错性 ，没有单点故障。网络的整体可靠性取决于冗余机制（如任务复制）的有效性和提供者的质量。 安全性是其主要挑战 ，因为网络由不可信节点组成，且依赖公共网络进行通信。但它也提供了 更强的抗审查能力 。用户对安全措施有更多控制权，但也承担更多责任。

7.5 可扩展性与弹性比较

• 中心化云 ：提供 **无缝的、按需