The Holy Grail of Crypto AI: Cutting-Edge Exploration of Decentralization Training

In the entire value chain of AI, model training is the most resource-consuming and technically demanding link, directly determining the upper limit of the model's capabilities and the actual application effects. Compared to the lightweight calls during the inference phase, the training process requires continuous large-scale computing power investment, complex data processing workflows, and high-intensity optimization algorithm support, making it the true "heavy industry" of AI system construction. From an architectural paradigm perspective, training methods can be divided into four categories: centralized training, distributed training, federated learning, and the decentralized training that this article focuses on.

Centralized training is the most common traditional approach, completed by a single organization within a local high-performance cluster, where all training processes—from hardware, underlying software, cluster scheduling systems, to all components of the training framework—are coordinated by a unified control system. This architecture of deep collaboration optimizes the efficiency of memory sharing, gradient synchronization, and fault tolerance mechanisms, making it very suitable for training large-scale models like GPT and Gemini, with advantages of high efficiency and controllable resources. However, it also faces issues such as data monopolization, resource barriers, energy consumption, and single-point risks.

Distributed training is the mainstream method for training large models today. Its core is to decompose the model training tasks and distribute them to multiple machines for collaborative execution, in order to break through the computing and storage bottlenecks of a single machine. Although it has "distributed" characteristics in a physical sense, it is still controlled, scheduled, and synchronized by centralized institutions, often operating in high-speed local area network environments, and using NVLink high-speed interconnect bus technology, with the master node coordinating various sub-tasks. Mainstream methods include:

• Data parallelism: each node trains different data parameters shared, model weights need to match
• Model parallelism: deploying different parts of the model on different nodes to achieve strong scalability.
• Pipeline parallelism: Staged serial execution to improve throughput
• Tensor Parallelism: Fine-grained segmentation of matrix computation to enhance parallel granularity.

Distributed training is a combination of "centralized control + distributed execution", analogous to a single boss remotely directing multiple "office" employees to collaborate on completing tasks. Currently, almost all mainstream large models are trained in this way.

Decentralization training represents a more open and censorship-resistant future path. Its core feature lies in: multiple mutually distrustful nodes ( which may be personal computers, cloud GPUs, or edge devices ) collaborating to complete training tasks without a central coordinator, typically driven by protocols for task distribution and cooperation, and leveraging cryptographic incentive mechanisms to ensure the honesty of contributions. The main challenges faced by this model include:

• Heterogeneous devices and segmentation difficulties: High coordination difficulty among heterogeneous devices, low task segmentation efficiency.
• Communication efficiency bottleneck: Network communication is unstable, and gradient synchronization bottleneck is obvious.
• Lack of Trusted Execution: The absence of a trusted execution environment makes it difficult to verify whether nodes are genuinely participating in the computation.
• Lack of unified coordination: no central dispatcher, complex task distribution and exception rollback mechanisms

Decentralization training can be understood as: a group of global volunteers contributing computing power to collaboratively train models, but "truly feasible large-scale decentralization training" remains a systematic engineering challenge, involving multiple aspects such as system architecture, communication protocols, cryptographic security, economic mechanisms, and model validation. However, whether it can achieve "collaborative effectiveness + incentive honesty + correct results" is still in the early prototype exploration stage.

Federated learning serves as a transitional form between distributed and Decentralization, emphasizing local data retention and centralized aggregation of model parameters, suitable for privacy-compliant scenarios like healthcare and finance (. Federated learning possesses the engineering structure of distributed training and local collaborative capabilities, while also leveraging the data dispersion advantages of Decentralization training, but it still relies on trustworthy coordinating entities and does not have fully open and censorship-resistant characteristics. It can be viewed as a "controlled Decentralization" solution in privacy-compliant scenarios, relatively mild in terms of training tasks, trust structures, and communication mechanisms, making it more suitable as a transitional deployment architecture for the industry.

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The Boundaries, Opportunities, and Real Paths of Decentralization Training

From the perspective of training paradigms, Decentralization training is not suitable for all types of tasks. In certain scenarios, due to the complex structure of tasks, extremely high resource demands, or significant collaboration difficulties, it is inherently unsuitable for efficient completion among heterogeneous, trustless nodes. For example, large model training often relies on high memory, low latency, and high bandwidth, making it difficult to effectively split and synchronize in an open network; tasks with strong data privacy and sovereignty restrictions are limited by legal compliance and ethical constraints, making open sharing impossible; and tasks lacking a basis for collaborative incentives lack external participation motivation. These boundaries together constitute the current realistic limitations of Decentralization training.

However, this does not mean that decentralization training is a false proposition. In fact, in lightweight, easily parallelizable, and incentivizable task types, decentralization training shows clear application prospects. These include, but are not limited to: LoRA fine-tuning, behavior alignment post-training tasks such as RLHF, DPO), data crowdsourcing training and labeling tasks, training of resource-controllable small foundational models, and collaborative training scenarios involving edge devices. These tasks generally exhibit characteristics of high parallelism, low coupling, and tolerance for heterogeneous computing power, making them very suitable for collaborative training through P2P networks, Swarm protocols, distributed optimizers, and other methods.

Decentralization Training Classic Project Analysis

Currently, the representative blockchain projects in the forefront fields of Decentralization training and federated learning mainly include Prime Intellect, Pluralis.ai, Gensyn, Nous Research, and Flock.io. In terms of technological innovation and engineering implementation difficulty, Prime Intellect, Nous Research, and Pluralis.ai have proposed many original explorations in system architecture and algorithm design, representing the cutting-edge direction of current theoretical research; while Gensyn and Flock.io have relatively clear implementation paths, with initial engineering progress already visible. This article will sequentially analyze the core technologies and engineering architectures behind these five projects, and further explore their differences and complementary relationships in the Decentralization AI training system.

( Prime Intellect: Verifiable Training Trajectories for Reinforcement Learning Collaborative Networks Pioneers

Prime Intellect is committed to building a trustless AI training network that allows anyone to participate in training and receive credible rewards for their computational contributions. Prime Intellect aims to create a verifiable, open, and fully incentivized AI Decentralization training system through the three major modules: PRIME-RL, TOPLOC, and SHARDCAST.

)# 01、Prime Intellect Protocol Stack Structure and Key Module Value

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(# 02, Detailed Explanation of Prime Intellect Training Key Mechanisms

PRIME-RL: Decoupled Asynchronous Reinforcement Learning Task Architecture

PRIME-RL is a task modeling and execution framework tailored for decentralized training scenarios by Prime Intellect, specifically designed for heterogeneous networks and asynchronous participation. It employs reinforcement learning as a priority adaptation object, structurally decoupling the training, inference, and weight upload processes, allowing each training node to independently complete task loops locally and collaborate with validation and aggregation mechanisms through standardized interfaces. Compared to traditional supervised learning processes, PRIME-RL is better suited for achieving flexible training in environments without centralized scheduling, reducing system complexity while laying the foundation for supporting multi-task parallelism and policy evolution.

TOPLOC: Lightweight Training Behavior Verification Mechanism

TOPLOC)Trusted Observation & Policy-Locality Check### is a core mechanism for verifiable training proposed by Prime Intellect, used to determine whether a node has truly completed effective policy learning based on observational data. Unlike heavy-weight solutions like ZKML, TOPLOC does not rely on full model recomputation, but instead completes lightweight structural verification by analyzing the local consistency trajectories between "observation sequences ↔ policy updates." It transforms the behavioral trajectories during the training process into verifiable objects for the first time, representing a key innovation for achieving trustless training reward allocation, and provides a feasible path for constructing auditable and incentivized Decentralization collaborative training networks.

SHARDCAST: Asynchronous Weight Aggregation and Propagation Protocol

SHARDCAST is a weight propagation and aggregation protocol designed by Prime Intellect, optimized for real network environments that are asynchronous, bandwidth-limited, and have variable node states. It combines a gossip propagation mechanism with local synchronization strategies, allowing multiple nodes to continuously submit partial updates in a non-synchronized state, achieving progressive convergence of weights and multi-version evolution. Compared to centralized or synchronous AllReduce methods, SHARDCAST significantly enhances the scalability and fault tolerance of Decentralization training, serving as the core foundation for establishing stable weight consensus and ongoing training iterations.

OpenDiLoCo: Sparse Asynchronous Communication Framework

OpenDiLoCo is a communication optimization framework independently implemented and open-sourced by the Prime Intellect team based on the DiLoCo concept. It is designed specifically to address common challenges in decentralized training, such as bandwidth limitations, device heterogeneity, and node instability. Its architecture is based on data parallelism, constructing sparse topologies like Ring, Expander, and Small-World to avoid the high communication overhead of global synchronization, relying only on local neighbor nodes to complete model co-training. By combining asynchronous updates and fault tolerance mechanisms, OpenDiLoCo allows consumer-grade GPUs and edge devices to stably participate in training tasks, significantly enhancing the participatory nature of global collaborative training, and is one of the key communication infrastructures for building decentralized training networks.

PCCL: Collaborative Communication Library

PCCL(Prime Collective Communication Library) is a lightweight communication library tailored by Prime Intellect for a decentralized AI training environment, aimed at addressing the adaptation bottlenecks of traditional communication libraries in heterogeneous devices and low-bandwidth networks. PCCL supports sparse topology, gradient compression, low-precision synchronization, and checkpoint recovery, and can run on consumer-grade GPUs and unstable nodes, serving as the underlying component that supports the asynchronous communication capabilities of the OpenDiLoCo protocol. It significantly enhances the bandwidth tolerance and device compatibility of the training network, paving the way for building a truly open and trustless collaborative training network by addressing the "last mile" of communication infrastructure.

(# 03. Prime Intellect Incentive Network and Role Division

Prime Intellect has built a permissionless, verifiable training network with economic incentives, allowing anyone to participate in tasks and earn rewards based on real contributions. The protocol operates based on three core roles:

• Task initiator: define the training environment, initial model, reward function, and validation criteria
• Training Node: Execute local training, submit weight updates and observation trajectories
• Validator Nodes: Use the TOPLOC mechanism to verify the authenticity of training behaviors and participate in reward calculation and strategy aggregation.

The core process of the protocol includes task publishing, node training, trajectory verification, weight aggregation ) SHARDCAST ### and reward distribution, forming an incentive closed loop around "real training behavior".

(# 04, INTELLECT-2: The release of the first verifiable Decentralization training model.

Prime Intellect released INTELLECT-2 in May 2025, which is the world's first large-scale reinforcement learning model trained collaboratively by asynchronous, trustless decentralized nodes, with a parameter scale of 32B. The INTELLECT-2 model was completed through the collaborative training of over 100 GPU heterogeneous nodes spread across three continents, utilizing a fully asynchronous architecture, with a training duration exceeding 400 hours, demonstrating the feasibility and stability of the asynchronous collaboration network. This model is not only a breakthrough in performance but also embodies the "training is consensus" paradigm proposed by Prime Intellect.