In the narrative of Web3, 'decentralization' often focuses on asset ownership and transaction ledgers, yet few pay attention to the decentralization of 'computing resources'—hundreds of millions of personal devices (computers, servers, and even smart terminals) generate massive idle computing power every day. This power is like 'idle houses in the digital age,' long trapped in a state of dormant value due to a lack of efficient organization and distribution mechanisms. Meanwhile, application parties in the blockchain ecosystem that require zero-knowledge proof (ZK) services have to rely on centralized service providers for expensive computing power, falling into the dilemma of 'strong demand for computing power but mismatched supply.'
Lagrange's innovation essentially injects the logic of 'sharing economy' into the ZK computation field: it acts like the 'Airbnb of computing power' in the Web3 world, organizing dispersed idle computing power into a decentralized network, providing low-cost ZK services to application parties while allowing computing power providers to obtain reasonable earnings. This 'computing power sharing + ZK proof' integration model not only solves the efficiency and cost issues of ZK computation but also reconstructs the value distribution rules of Web3 computing resources—allowing ordinary users' idle devices to participate in the core infrastructure construction of blockchain, truly realizing 'computing power equals rights.'
I. The 'Web3 Solution' of Computing Power Sharing: A Paradigm Shift from Centralized Monopoly to Distributed Collaboration
Before the emergence of Lagrange, the supply of computing power for ZK calculations was long monopolized by centralized service providers. These providers built large data centers to deploy high-performance servers centrally, providing ZK proof generation services to application parties. This model has two core issues:
One is the waste of computing resources. The supply of computing power from centralized data centers is 'rigid'—regardless of application demand fluctuations, servers must run 24 hours a day, leading to significant idle computing power during low-demand periods; while the idle computing power of personal devices (e.g., computers in sleep mode at night, servers during work breaks) is massive, it cannot be effectively utilized due to its dispersion and lack of centralized scheduling. It is estimated that the idle computing power of global personal devices annually can satisfy the ZK computing needs of the Web3 ecosystem more than three times, creating a contradiction of 'centralized waste + distributed idleness' that keeps ZK computing costs high.
Two is the imbalance of value distribution. Centralized service providers, relying on their monopoly on computing power, charge application parties high fees (e.g., a certain service provider charges 0.05-0.1 ETH for a single ZK proof), with most earnings being intercepted by the service provider, while the actual providers of computing power (data center employees, hardware investors) receive only meager returns; ordinary users, even if they possess idle computing power, cannot participate in value distribution due to a lack of access channels. This model of 'monopolists taking most of the cake' is contrary to the Web3 philosophy of 'decentralization and shared building.'
Lagrange provides Web3 solutions to these problems through a combination of 'decentralized node networks + shared economic incentives,' with its core logic decomposed into two steps:
1. The 'democratization' of computing power access: allowing ordinary devices to participate in ZK calculations
Traditional ZK networks have extremely high hardware requirements for nodes, often requiring professional servers (e.g., equipped with high-end GPUs and large memory) to operate, excluding ordinary users. Lagrange significantly lowers the threshold for computing power access through 'task sharding' and 'lightweight node' technology:
- Task sharding: Breaking down complex ZK computation tasks (like generating proofs for 1,000 transactions) into hundreds of simple subtasks (like individually calculating the hash of a transaction), each requiring only ordinary computers to complete, with no need for specialized hardware;
- Lightweight nodes: Developing a node client with a size of only 50MB, allowing users to join the network without downloading complete blockchain data; they only need to install the client and stake a small amount of mainstream tokens (like ETH or USDC supported by EigenLayer) to become network nodes, utilizing idle time for computation.
This 'democratization' design allows computing power access to no longer be the exclusive domain of professional institutions. By 2024, among Lagrange's over 500 nodes, 72% come from idle devices of individual users (such as home computers and laptops). These nodes are distributed across more than 30 countries worldwide, forming a 'distributed computing power network'—during the day, users work and entertain on their computers; at night, devices automatically connect to the Lagrange network to participate in ZK computation tasks, turning previously wasted computing power into actual earnings.
2. Fairness in value distribution: Distributing earnings based on computing power contribution
To ensure that computing power providers receive reasonable returns, Lagrange has designed an 'incentive mechanism based on contribution distribution,' with core rules including:
- Earnings linked to computing power contribution: The earnings of nodes are determined by the 'number of completed subtasks' and 'calculation accuracy'—the more subtasks completed and the more accurate the results, the more token rewards earned;
- Staking is positively correlated with earnings: The more assets nodes stake (within reasonable limits), the higher their task allocation weight, allowing them to earn more rewards for the same computing power, thus encouraging long-term participants;
- Zero-threshold withdrawals: Rewards are distributed in the form of mainstream stablecoins or ecosystem tokens, allowing users to withdraw to personal wallets anytime without going through third-party platforms, avoiding revenue interception.
This distribution mechanism allows ordinary users' idle computing power to truly generate value. For example, a university student connects their idle gaming laptop to the Lagrange network, running it for 4 hours each night, earning approximately $80-120 per month; a small studio connects 10 idle servers to the network, earning over $2,000 per month. Compared to the 'fixed salary' model of centralized data centers, Lagrange's model links the earnings of computing power providers directly to their contributions, aligning more closely with Web3's 'rights-sharing' logic.
II. The 'Chemical Reaction' of ZK + Shared Computing Power: How to Solve the 'Triple Anxiety' of Application Parties
For Web3 application parties, using ZK technology often entails facing 'cost anxiety,' 'security anxiety,' and 'adaptation anxiety'—costs are so high that they are unbearable, security relies on a single service provider, and technical adaptation is complex and time-consuming. Lagrange's 'ZK + shared computing power' model specifically addresses these three anxieties, enabling application parties to confidently use, effectively use, and make the most of ZK technology.
1. Cost anxiety: From 'high-price monopoly' to 'affordable sharing'
The charging model of centralized ZK service providers has deterred many small and medium application parties. A certain DeFi project once calculated that if it used centralized services to process ZK proofs for 10,000 transactions daily, the monthly cost would be about $30,000, accounting for more than 15% of the project's monthly revenue; however, after integrating Lagrange, the monthly cost dropped to $3,000 due to the cost advantage of shared computing power, reducing costs by 90%.
Lagrange's low cost stems from two core advantages:
- Low computing power costs: The operational costs of personal idle devices are almost zero (no additional payments for space or electricity), and the overall computing power cost of the network is only one-tenth that of centralized data centers;
- No middleman price markup: Application parties directly initiate computing power requests to the node network through smart contracts without going through centralized service providers, avoiding 'middleman profit margins.'
This 'affordable sharing' model brings ZK technology from being 'exclusive to high-end applications' to 'inclusive services.' For instance, a newly emerging NFT platform originally planned to abandon the feature of 'on-chain verification of NFT authenticity' due to high ZK costs; after integrating Lagrange, the daily ZK service cost dropped from $500 to $50, ultimately enabling this feature, increasing user trust by 40% and doubling NFT transaction volume.
2. Security anxiety: From 'single-point dependence' to 'network consensus'
When application parties use centralized ZK services, they always face the risk of 'single point of failure'—if the service provider's server is attacked or the data is tampered with, the generated ZK proof will lose credibility, potentially leading to asset losses for the application parties. In 2022, a certain cross-chain protocol was attacked by hackers using a centralized ZK service provider, resulting in $20 million worth of assets being unable to cross-chain normally, causing user panic.
Lagrange's decentralized network fundamentally solves the problem of 'single-point dependence':
- Multi-node verification: The same subtask is assigned to multiple nodes for parallel computation, and only if more than 2/3 of the nodes produce consistent results will it be included in the final proof, thus avoiding malfeasance by a single node;
- Economic penalty mechanism: Nodes must stake assets to participate in computation; if they are found to submit false results, their staked assets will be deducted, and they will be expelled from the network, thus curbing malicious behavior from an economic standpoint;
- Open-source transparency: The network's task allocation algorithms and verification logic are completely open-source, allowing any developer to audit the code to ensure there are no backdoors or vulnerabilities.
This 'network consensus'-style security assurance alleviates application parties' concerns about 'service provider malfeasance.' A security officer from a DeFi lending project that integrated Lagrange stated: 'In the past, we had to monitor the status of centralized service providers every day, worrying about potential failures; now we only need to focus on the operation of smart contracts, and the network's security is jointly guaranteed by hundreds of nodes, significantly enhancing our sense of security.'
3. Adaptation anxiety: From 'complex customization' to 'one-click access'
The adaptation threshold of traditional ZK technology is extremely high. Application parties need to assemble a professional ZK development team, understand complex algorithm logic (such as constraint system design and trusted initialization), and customize development for different application scenarios, which often takes 3-6 months. Many small and medium application parties have to abandon using ZK technology due to a lack of technical capability.
Lagrange reduces the adaptation difficulty to 'one-click access' through 'scenario-based SDK + ecological collaboration':
- Scenario-based SDK: Developing standardized SDKs (Software Development Kits) for mainstream scenarios such as DeFi transactions, cross-chain verification, and AI inference, allowing application parties to quickly implement ZK functionality without needing to understand ZK algorithm details, simply by calling simple interfaces in the SDK (like 'generateTransactionProof()' or 'verifyCrossChainData()');
- Ecological cooperation adaptation: Collaborating with ecological partners such as EigenLayer and cross-chain protocols to complete interface adaptations in advance. For instance, if an application party uses EigenLayer's restaking service, it can directly access Lagrange through EigenLayer's interface without additional development;
- Technical support services: Providing free technical documentation and community support, allowing developers to quickly obtain answers to problems they encounter in the Discord community, further reducing adaptation difficulty.
This 'low-threshold adaptation' has significantly accelerated the spread of ZK technology. A cross-chain bridge project was developed by a team of only three people, with no background in ZK technology. By using Lagrange's cross-chain verification SDK, they completed the integration of ZK functionality in just 10 days, achieving real-time verification of cross-chain transactions and significantly improving user experience.
III. The 'Breakthrough of Boundaries' in Computing Power Sharing: From ZK computation to Web3 trusted infrastructure
Lagrange's 'computing power sharing + ZK' model was initially born to address the pain points of ZK computation, but as the ecosystem develops, its value has transcended the ZK field and begun to extend toward 'Web3 trusted infrastructure'—providing 'efficient, trustworthy, low-cost' computing services for more scenarios through a shared computing power network, promoting Web3's transition from 'asset decentralization' to 'computing decentralization.'
1. Verifiable AI inference: Allowing Web3 AI to escape the 'black box dilemma'
The core issue facing current Web3 AI applications is the 'opacity of the inference process'—the decision logic of AI models (such as risk scoring in DeFi or value assessment of NFTs) is a 'black box.' Users cannot confirm whether the model operates according to preset rules or verify if the results have been tampered with. Lagrange provides 'verifiability' for AI inference through a shared computing power network:
- Distributed computation of inference processes: Breaking down the inference tasks of AI models into subtasks, which are computed in parallel by multiple nodes, each generating ZK proofs for intermediate results;
- Result aggregation and verification: Lagrange's co-processor aggregates proofs of all intermediate results into a complete 'AI inference ZK proof' for submission on-chain;
- User verifiability: Users can verify each step of AI inference through on-chain smart contracts, confirming that the inference process has not been tampered with, escaping the 'black box dilemma.'
For instance, after a certain Web3 insurance project integrated Lagrange, it used the shared computing power network to verify the inference process of its AI claims model: after users submitted claims, the risk assessment process of the AI model was distributed across 50 nodes, and the generated ZK proof was stored on-chain. Users could view the calculation basis for the claim amount in real time, resulting in a 65% decrease in complaint rates and significantly enhancing trust.
2. Trustworthy on-chain IoT data: Connecting the physical world with Web3
Data generated by IoT devices (such as smart meters and logistics sensors) is key to integrating Web3 with the real world, but putting data on-chain faces challenges such as 'difficulty in verifying authenticity' and 'high costs.' Lagrange's shared computing power network offers a new solution for on-chain IoT data:
- Distributed data preprocessing: The raw data generated by IoT devices (such as smart meter readings and cargo locations) is first transmitted to Lagrange's shared computing power network, where nodes clean and verify the data, generating ZK proofs of data authenticity;
- Lightweight on-chain: Only the data hash and ZK proof are put on-chain, while the raw data is stored off-chain, significantly reducing on-chain costs;
- Real-time verification: Application parties (e.g., energy management platforms, logistics tracking projects) can quickly verify data authenticity through on-chain proofs without trusting a single data provider.
After collaborating with Lagrange, a Web3 energy project achieved credible on-chain data for smart meter readings: the readings from over 1,000 smart meters worldwide were verified and generated ZK proofs by Lagrange's shared computing power network. The daily on-chain cost dropped from $2,000 to $150, while the authenticity of the data was guaranteed at 100%, providing a credible foundation for energy transactions among users.
3. Decentralized storage verification: Enhancing storage security
Web3 decentralized storage projects (such as IPFS) face the issue of 'whether storage nodes genuinely store data'—some nodes may falsely claim to store data but do not actually save it, leading to data loss. Lagrange supports storage verification through its shared computing power network:
- Random checks and proof generation: Lagrange's node network regularly conducts random checks on storage nodes, requiring them to generate 'proof of data possession' (PoH) and convert the proof into ZK format;
- On-chain verification: Once the ZK proof is on-chain, smart contracts automatically verify whether the storage nodes genuinely store the data; if they fail the verification, the storage nodes will be punished;
- Low-cost scalability: The low-cost advantage of the shared computing power network allows storage verification to cover a large number of storage nodes, avoiding the high-cost issues of centralized verification.
This 'storage verification + ZK' model significantly enhances the security of decentralized storage. After integrating Lagrange, the false rate of storage nodes in a certain IPFS ecological project dropped from 15% to below 2%, significantly improving data reliability.
IV. The 'Future Exam Question' of Computing Power Sharing: Balancing Scale and Security
Although Lagrange's 'computing power sharing' model has broad prospects, it also faces three major challenges as the ecosystem scales: 'node management,' 'algorithm compatibility,' and 'governance decentralization.' The solutions to these challenges will determine whether it can truly become a trusted infrastructure for Web3.
1. Node management: How to address efficiency challenges brought by 'scale expansion'
As the number of nodes increases (expected to exceed 1,000 by 2025), the collaborative efficiency of the network may decline—task allocation delays, increased time for result aggregation, and rising communication costs between nodes. Currently, Lagrange is addressing this with a 'dynamic sharding algorithm': automatically adjusting the number of shards and node groups based on node distribution and task volume to ensure computational efficiency; however, once the number of nodes exceeds a critical point, it may be necessary to introduce a 'hierarchical node' mechanism:
- Computing nodes: Responsible for processing subtasks, allowing low-threshold access;
- Aggregation nodes: Responsible for verifying and aggregating intermediate results, requiring higher staking and hardware requirements;
- Super nodes: Responsible for overall network scheduling and anomaly handling, elected by community vote.
By implementing hierarchical division of labor, we can balance 'scale' and 'efficiency,' ensuring the network operates efficiently even as the number of nodes grows.
2. Algorithm compatibility: How to adapt to diverse ZK demands
Currently, the ZK field has various algorithms such as ZK-SNARK, ZK-STARK, and PLONK, with different application scenarios requiring different algorithms (e.g., ZK-STARK does not require trusted initialization and is suitable for permissionless scenarios; ZK-SNARK has a small proof size and is suitable for on-chain verification). Currently, Lagrange mainly supports ZK-SNARK, which makes it difficult to meet all needs. In the future, it is necessary to construct an 'algorithm abstraction layer':
- Application parties remain unaware: Application parties initiate requests through standardized interfaces without needing to pay attention to the underlying algorithms;
- Automatic algorithm selection: The algorithm abstraction layer automatically selects the appropriate ZK algorithm based on application scenarios (e.g., whether trusted initialization is needed, proof size requirements);
- Dynamic node adaptation: The node network automatically allocates nodes with corresponding computing capabilities based on the algorithm type, ensuring computational efficiency.
By achieving algorithm compatibility, Lagrange's services can cover a wider range of scenarios, attracting more applications to integrate.
3. Governance decentralization: How to avoid monopolies caused by 'computing power concentration'
As the network develops, some nodes may concentrate computing power and earnings by increasing hardware investments and staking amounts, forming 'computing power concentration,' which may affect the fairness of network governance. To avoid this issue, Lagrange needs to promote 'community governance':
- Issuing governance tokens: Token holders can vote to determine network rules (such as reward distribution ratios, algorithm access, and node admission standards);
- Decentralized proposal mechanism: Any ecological participant (nodes, application parties, users) can submit proposals, which will be implemented after being approved by community vote;
- Computing power cap restrictions: Smart contracts limit the computing power share of individual nodes or node clusters (e.g., no more than 5% of total computing power) to avoid concentration of computing power.
Only by achieving governance decentralization can we ensure that Lagrange's 'computing power sharing' model remains fair and sustainable in the long term.
V. Conclusion: Computing Power Sharing, the Next Piece of the Web3 Decentralization Puzzle
Lagrange's value lies not only in optimizing the efficiency and cost of ZK computation but also in combining the concept of 'sharing economy' with Web3 technology, reconstructing the value distribution rules of computing resources—allowing ordinary users' idle devices to participate in blockchain infrastructure construction, enabling application parties to obtain trustworthy computing services at low costs, and facilitating a critical step toward the decentralization of computing in Web3.
In the development of Web3, 'decentralization' should not only remain at the asset level but also extend to the core resources supporting ecological operation—computing power, storage, and bandwidth. Lagrange's practice proves that through the fusion of 'sharing + technology,' the decentralization of these resources is feasible and can create greater ecological value.
In the future, when Lagrange's shared computing power network covers more scenarios (AI, IoT, storage), and more users participate in the construction of Web3 infrastructure through idle devices, Web3 can truly realize the vision of 'co-building and co-sharing by all.' The 'computing power sharing' model represented by Lagrange will also become an indispensable piece in the puzzle of Web3 decentralization, driving the industry toward a fairer, more efficient, and more trustworthy direction.@Lagrange Official #lagrange $LA
