Original Author: Jtsong.eth (Ø,G) (@Jtsong2)
Recent Cryptocurrency Investment Research Think Tank @MessariCrypto A comprehensive in-depth research report on 0G has been released. This article is a condensed Chinese summary version:
【Core Summary】
With the outbreak of the decentralized artificial intelligence (DeAI) track in 2026,0G (Zero Gravity) With its groundbreaking technical architecture, it completely resolves the long-standing challenge of Web3's inability to support large-scale AI models. Its core differentiator can be summarized as follows:
Ultra-fast Performance Engine (50 Gbps Throughput)By achieving logical decoupling and multi-level parallel sharding, 0G achieves significantly better performance compared to traditional DA layers (such as Ethereum and Celestia). 600,000 times 's performance breakthrough, becoming the only protocol globally capable of supporting real-time distribution of ultra-large-scale models like DeepSeek V3.
dAIOS Modular Architecture: It pioneered a four-layer collaborative operating system paradigm of "settlement, storage, data availability (DA), and computation," breaking through the traditional blockchain's "storage deficit" and "computational lag," and achieving an efficient closed loop of AI data flow and execution flow.
AI Native Trusted Execution Environment (TEE + PoRA)By deeply integrating Trusted Execution Environments (TEE) with Proof of Random Access (PoRA), 0G not only addresses the "hot storage" demands of massive data, but also constructs a trustless, privacy-protected environment for AI inference and training, achieving a leap from "ledger" to the "foundation of digital life."
Chapter 1: Macro Context — The "Decoupling and Reconstruction" of AI and Web3
In the context of artificial intelligence entering the era of large models, data, algorithms, and computing power have become core production factors. However, existing traditional blockchain infrastructures (such as Ethereum and Solana) are facing a serious "performance mismatch" when supporting AI applications.
1. Limitations of Traditional Blockchains: Throughput and Storage Bottlenecks
Traditional Layer 1 blockchain designs were originally intended to handle financial ledger transactions, not to carry TB-level AI training datasets or support high-frequency model inference tasks.
Storage deficitThe data storage cost on chains like Ethereum is extremely high, and there is a lack of native support for unstructured big data, such as model weight files and video datasets.
Throughput bottleneckEthereum's data availability (DA) bandwidth is only about 80KB/s, and even after the EIP-4844 upgrade, it is still far from meeting the GB-level throughput requirements for real-time inference by large language models (LLMs).
Calculate lagAI inference requires extremely low latency (in the millisecond range), whereas blockchain consensus mechanisms typically operate on the order of seconds, making "on-chain AI" nearly infeasible under existing architectures.
2. The Core Mission of 0G: Breaking Down the "Data Wall"
The AI industry is currently monopolized by centralized giants, forming a de facto "Data Wall." This has led to restricted data privacy, unverifiable model outputs, and high rental costs.0G (Zero Gravity) The emergence of marks the deep integration of AI and Web3. It no longer merely regards the blockchain as a ledger for storing hash values, but instead proceeds through...Modular ArchitectureDecouple the "data flow, storage flow, and computation flow" required by AI. The core mission of 0G is to break the centralized black box and make AI assets (data and models) sovereignly owned public goods through decentralized technologies.
After understanding this macro-level misalignment, we need to delve deeper into how 0G tackles these fragmented pain points layer by layer through a rigorous four-tiered architecture.
Chapter 2: Core Architecture — The Four-Layer Collaboration of the Modular 0G Stack
0G is not simply a single blockchain, but is defined as dAIOS (Decentralized AI Operating System)The core of this concept lies in providing AI developers with a complete protocol stack similar to an operating system. Through deep collaboration across its four-layer architecture, it achieves exponential performance improvements.
1. Analysis of the Four-Layer Architecture of dAIOS
The 0G Stack ensures that each layer can scale independently by decoupling execution, consensus, storage, and computation:
2. 0G Chain: Performance Foundation Based on CometBFT
As the neural hub of dAIOS,0G Chain Highly optimized CometBFT is a high-performance, open-source consensus engine that is widely used in blockchain systems Consensus mechanism. Its innovation lies in separating the execution layer from the consensus layer, and significantly reducing the waiting time for block production through pipelining parallel processing and the ABCI modular design. Performance MetricsAccording to the latest benchmark tests, 0G Chain can achieve throughput of 11,000+ TPS with high throughput and sub-second finality. This high performance ensures that on-chain settlement will not become a bottleneck when large-scale AI agents interact frequently.
3. Decoupled Collaboration between 0G Storage and 0G DA
The technical moat of 0G lies in its "dual-channel" design, which separates data publishing from persistent storage:
0G DA: Focuses on the fast broadcasting and sampling verification of blob data. It supports a maximum of approximately 32.5 MB per blob. By utilizing erasure coding technology, data remains available even if some nodes are offline.
0G Storage refers to a storage capacity of zero gigabytes. It typicallyImmutable data is handled through the "Log Layer," and dynamic state is handled through the "Key-Value (KV) Layer."
This four-layer collaborative architecture provides fertile ground for high-performance DA layers. Next, we will delve into the most impressive part of the 0G core engine—the high-performance DA technology.
Chapter 3: Deep Dive into the Technology of High-Performance DA Layer (0G DA)
In the decentralized AI ecosystem of 2026, data availability (DA) is no longer just "proof of publication," but must support real-time pipelines for PB-level AI weight files and training datasets.
3.1 Logical Decoupling and Physical Collaboration: Generational Evolution of the "Dual-Channel" Architecture
The core advantage of 0G DA originates from its unique "dual-channel" architecture: separating data publishing (Data Publishing)andData storage (Data Storage) is completely decoupled logically, but achieves efficient collaboration at the physical node level.
Logical DecouplingUnlike traditional DA layers that conflate data availability with long-term storage, 0G DA is only responsible for verifying the short-term availability of data blocks, while the persistence of massive data is delegated to 0G Storage.
Physical Collaboration: Storage Node UtilizationProof of Random Access (PoRA) ensures the actual existence of data, while DA nodes operate through a sharding-based consensus network.Ensuring transparency, it has achieved "immediate verification upon issuance and integrated storage and verification."
3.2 Performance Benchmark: Data-Level Competition at the Forefront
0G DA's breakthrough in throughput directly defines the performance boundaries of decentralized AI operating systems. The table below presents a comparison of technical parameters between 0G and mainstream DA solutions:
3.3 Technical Foundation for Real-Time Availability: Erasure Coding and Multi-Consensus Sharding
To support massive AI data, 0G introducedErasure Coding and Multi-Consensus Sharding:
Erasure Code Optimization: By adding redundant proofs, it is possible to recover complete information by sampling extremely small data fragments, even when a large number of nodes in the network are offline.
Multi-Consensus Sharding0G abandons the linear logic of a single chain handling all data availability (DA). By horizontally scaling the consensus network, the total throughput increases as the number of nodes grows.Linear growthIn the 2026 field test, it supported tens of thousands of Blob verification requests per second, ensuring the continuity of the AI training pipeline.
Having a high-speed data channel alone is not enough; AI also requires low-latency "brain storage" and a secure, private "execution space," which leads to the introduction of a dedicated AI optimization layer.
Chapter 4: AI-Specific Optimization and Enhanced Secure Computing Power
4.1 Addressing Delay Anxiety in AI Agents
For AI agents that execute strategies in real time, data retrieval latency is the make-or-break threshold determining their survival.
Cold and Hot Data Separation Architecture0G Storage is internally divided intoImmutable Log Layer and Mutable State Layer (KV Layer)Hot data is stored in the high-performance KV layer, supporting sub-second random access.
High-performance indexing protocolBy utilizing a distributed hash table (DHT) and dedicated metadata index nodes, AI agents can locate the required model parameters in milliseconds.
4.2 TEE Enhancement: The Final Piece in Building Trustless AI
0G was fully introduced in 2026. TEE (Trusted Execution Environment) Security upgrade.
Privacy-preserving computationModel weights and user inputs are processed within an "isolated zone" inside the TEE. Even the node operator cannot窥视 the computation process.
Result VerifiabilityTEE-generated remote silent attestation is submitted together with the computation results to the 0G Chain, ensuring that the results are produced by a specific, untampered model.
4.3 Vision Realization: The Leap from Storage to Operating System
AI agents are no longer isolated scripts, but rather possess...Sovereign Identity (iNFT Standard)Protected Memory (0G Storage)andA digital life entity of Trusted Execution Environment (TEE Compute). This closed-loop system eliminates the monopoly of centralized cloud vendors over AI, marking the entry of decentralized AI into the era of large-scale commercialization.
However, to support these "digital lives," the underlying distributed storage must undergo a performance revolution—from "cold" to "hot."
Chapter 5: Innovation in the Distributed Storage Layer — A Paradigm Shift from "Cold Archiving" to "Hot Performance"
The core innovation of 0G Storage lies in breaking through the performance limitations of traditional distributed storage.
1. Two-layer architecture: Decoupling of the Log Layer and the KV Layer
Log Layer (Stream Data Processing): Specifically designed for unstructured data (such as training logs, datasets). By using an append-only writing mode, it ensures millisecond-level synchronization of massive data across distributed nodes.
KV Layer (Index and State Management)For structured data, it provides high-performance indexing support. When retrieving model parameter weights (Weights), it reduces response latency to the millisecond level.
2. PoRA (Proof of Random Access): Anti-Sybil Attack and Verification System
To ensure storage authenticity, 0G has introduced PoRA (Proof of Random Access).
Anti-Sybil attackPoRA ties the mining difficulty directly to the actual physical storage space occupied.
VerifiabilityAllow the network to randomly "audit" nodes to ensure that data is not only stored but also kept in a hot, readily accessible state.
3. Performance Breakthrough: Engineering Implementation of Second-Level Search
By combining erasure coding with high-bandwidth DA channels, 0G achieves a retrieval leap from "minute-level" to "second-level." This "hot storage" capability delivers performance comparable to centralized cloud services.
This leap in storage performance provides a solid decentralized foundation for supporting models with hundreds of billions of parameters.
Chapter 6: Native AI Support — Decentralized Foundation for Hundreds of Billions of Parameter Models
1. AI Alignment Nodes: Guardians of AI Workflows
AI Alignment Nodes Responsible for monitoring the collaboration between storage nodes and service nodes. By verifying the authenticity of training tasks, it ensures that AI model operations do not deviate from the preset logic.
2. Support large-scale parallel I/O
Handling models with hundreds of billions or even trillions of parameters (such as Llama 3 or DeepSeek-V3) requires extremely high parallel I/O. 0G enables thousands of nodes to simultaneously process large-scale dataset reads through data slicing and multi-consensus sharding technologies.
3. Collaboration between Checkpoints and High-Bandwidth DA
Fault Recovery0G can quickly persist checkpoint files of hundreds of gigabytes in size.
Seamless RecoveryThanks to the 50 Gbps throughput ceiling, new nodes can instantly synchronize the latest checkpoint snapshots from the DA layer, solving the pain point of decentralized large model training being difficult to sustain in the long term.
Beyond the technical details, we must broaden our perspective to the entire industry and see how 0G is sweeping through the existing market.
Chapter 7: Competitive Landscape — Dimensional Supremacy and Differentiated Advantages of 0G
7.1 Horizontal Evaluation of Mainstream DA Solutions
7.2 Core Competence: Programmable DA and Vertically Integrated Storage
Eliminate transmission bottlenecksNative integrated storage layer, allowing AI nodes to directly retrieve historical data from the DA layer.
A leap in throughput of 50 Gbps: Several orders of magnitude faster than competitors, supporting real-time inference.
Programmable DA: Allows developers to customize data distribution strategies and dynamically adjust data redundancy levels.
This dimensional dominance heralds the rise of a vast economic entity, and token economics serves as the fuel driving this system.
Chapter 8: 2026 Ecosystem Outlook and Token Economics
With the smooth operation of the mainnet in 2025, 2026 will become a key milestone for the explosive growth of the 0G ecosystem.
8.1 $0G Token: Multi-Dimensional Value Capture Path
Resource Payment (Work Token): The only medium for accessing high-performance DA and storage space.
Security Deposit (Staking)Validators and storage providers must stake $0G to receive network revenue dividends.
Priority AssignmentDuring busy periods, the token holdings determine the priority of computational tasks.
8.2 2026 Ecological Incentives and Challenges
0G Program Launch "Gravity Foundation 2026" Special funds will focus on supporting the DeAI inference framework and data crowdfunding platform. Despite its technological leadership, 0G still faces challenges such asHigh hardware requirements for nodes、Eco-cold startandComplianceChallenges.