The Holy Grail of Crypto AI: Frontier Exploration of Decentralized Training

In the full value chain of AI, model training is the segment with the highest resource consumption and the highest technical threshold, directly determining the model's capability ceiling and actual application effectiveness. Compared to the lightweight calls during the inference phase, the training process requires continuous large-scale computing power input, 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 method, completed by a single organization within a local high-performance cluster, where all training processes are coordinated by a unified control system, including hardware, underlying software, cluster scheduling systems, and all components of the training framework. This deeply collaborative architecture 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 presents issues such as data monopolization, resource barriers, energy consumption, and single point of failure risks.

Distributed training is the mainstream method for training large models. Its core is to break down the model training tasks and distribute them to multiple machines for collaborative execution, in order to overcome the computing and storage bottlenecks of a single machine. Although it physically possesses the characteristics of "Decentralization," it is still centrally controlled and scheduled by a centralized organization, often operating in a high-speed local area network environment. Through NVLink high-speed interconnect bus technology, the main node coordinates all sub-tasks. Mainstream methods include:

• Data parallelism: each node trains different data with shared parameters, requiring matching model weights.
• Model parallelism: Deploy different parts of the model on different nodes to achieve strong scalability.
• Pipeline parallelism: Staged serial execution to improve throughput
• Tensor Parallelism: Fine-grained partitioning of matrix computations to enhance parallel granularity

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

Decentralization training represents a future path with greater openness and resistance to censorship. Its core feature lies in: multiple untrusted 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 backed by cryptographic incentive mechanisms to ensure the honesty of contributions. The main challenges faced by this model include:

• Device heterogeneity and partitioning difficulties: High difficulty in coordinating heterogeneous devices, low efficiency in task partitioning
• Communication efficiency bottleneck: network communication is unstable, gradient synchronization bottleneck is obvious
• Missing Trusted Execution: Lack 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 mechanism.

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

Federated learning, as a transitional form between distributed and Decentralization, emphasizes local data retention and centralized aggregation of model parameters, making it suitable for privacy-compliant scenarios such as healthcare and finance (. Federated learning has the engineering structure of distributed training and local collaborative capabilities, while also possessing the data dispersion advantages of Decentralization training; however, it still relies on trusted coordinating parties and does not possess the characteristics of complete openness and resistance to censorship. It can be seen 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 in the industry.

Decentralization Training: Boundaries, Opportunities, and Realistic Pathways

From the perspective of training paradigms, Decentralization training is not suitable for all types of tasks. In certain scenarios, due to complex task structures, 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 challenging to effectively partition and synchronize in an open network; tasks with strong data privacy and sovereignty restrictions are limited by legal compliance and ethical constraints, preventing open sharing; and tasks lacking a foundation for collaborative incentives lack external participation motivation. These boundaries collectively constitute the current realistic limitations of Decentralization training.

However, this does not mean that decentralized training is a false proposition. In fact, decentralized training shows clear application prospects in lightweight, easily parallelizable, and incentivized task types. These include, but are not limited to: LoRA fine-tuning, behavior alignment post-training tasks ) such as RLHF, DPO (, data crowdsourcing training and annotation tasks, training of small foundational models with controllable resources, and collaborative training scenarios involving edge devices. These tasks generally possess 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.

![The Holy Grail of Crypto AI: Cutting-edge Exploration of Decentralization Training])https://img-cdn.gateio.im/webp-social/moments-a8004f094fff74515470052b3a24617c.webp(

Decentralization Training Classic Project Analysis

Currently, in the forefront fields of Decentralization training and federated learning, representative blockchain projects 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 preliminary 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: A pioneer of verifiable training trajectories in reinforcement learning collaborative networks

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 of PRIME-RL, TOPLOC, and SHARDCAST.

01, The Structure of the Prime Intellect Protocol Stack and the Value of Key Modules

![The Holy Grail of Crypto AI: Frontline Exploration of Decentralization Training]###https://img-cdn.gateio.im/webp-social/moments-adb92bc4dfbaf26863cb0b4bb1081cd7.webp(

)# 02, Detailed Explanation of the Key Mechanisms of Prime Intellect Training

#PRIME-RL: Decoupled Asynchronous Reinforcement Learning Task Architecture

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

#TOPLOC: Lightweight Training Behavior Verification Mechanism

TOPLOC is a core mechanism for training verifiability proposed by Prime Intellect, used to determine whether a node has truly completed effective strategy learning based on observational data. Unlike heavyweight solutions like ZKML, TOPLOC does not rely on full model recomputation but completes lightweight structural verification by analyzing the local consistency trajectories between "observation sequence ↔ strategy update." It transforms the behavioral trajectories during the training process into verifiable objects for the first time, serving as a key innovation for achieving trustless training reward distribution and providing 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-constrained, and have variable node states. It combines a gossip propagation mechanism with a local synchronization strategy, allowing multiple nodes to continuously submit partial updates in an unsynchronized 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 building stable weight consensus and continuous 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 proposed by DeepMind. It is designed specifically to address challenges commonly encountered 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 solely on local neighbor nodes to complete model collaborative training. By combining asynchronous updates with a fault tolerance mechanism, OpenDiLoCo enables consumer-grade GPUs and edge devices to stably participate in training tasks, significantly enhancing the inclusivity of global collaborative training, and is one of the key communication infrastructures for building decentralized training networks.

#PCCL: Collaborative Communication Library

PCCL is a lightweight communication library tailored for the decentralized AI training environment by Prime Intellect, 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. It is 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, trustless collaborative training network and overcoming 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 receive 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 centered around "real training behavior."

![The Holy Grail of Crypto AI: Cutting-edge Exploration of Decentralization])https://img-cdn.gateio.im/webp-social/moments-69eb6c2dab3d6284b890285c71e7a47f.webp(

)# 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 model of reinforcement learning trained by asynchronous, trustless decentralized node collaboration, with a parameter scale of 32B. The INTELLECT-2 model was collaboratively trained by over 100 heterogeneous GPU nodes distributed across three continents, using a fully asynchronous architecture, with a training duration exceeding 400 hours, demonstrating the feasibility and stability of asynchronous collaborative networks. This model not only represents a breakthrough in performance but also marks the first systematic implementation of the "training is consensus" paradigm proposed by Prime Intellect. INTELLECT-2 integrates core protocol modules such as the PRIME-RL### asynchronous training structure(, TOPLOC) training behavior verification(, and SHARDCAST) asynchronous weight aggregation(, signifying that decentralized training networks have achieved training for the first time.