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

In the entire value chain of AI, model training is the stage with the highest resource consumption and the greatest technical threshold, directly determining the upper limit of the model's capabilities and its practical application effects. Compared to the lightweight calls in the inference stage, 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 decentralized training, which is the focus of this article.

Centralized training is the most common traditional method, completed by a single institution 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, with its core being the decomposition of the model training tasks, which are then distributed to multiple machines for collaborative execution, in order to break through the bottlenecks of single-machine computation and storage. Although it physically possesses "Decentralization" characteristics, it is still centrally controlled and scheduled by a centralized organization, often operating in a high-speed local area network environment, utilizing NVLink high-speed interconnect bus technology, with the main node coordinating various sub-tasks uniformly. 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: Execute in stages sequentially to improve throughput
• Tensor parallelism: fine-grained segmentation of matrix calculations to improve parallel granularity

Distributed training is a combination of "centralized control + distributed execution", analogous to the same 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 future path with greater openness and anti-censorship characteristics. Its core feature lies in: multiple distrustful nodes collaborating to complete training tasks without a central coordinator, typically driven by protocols for task distribution and collaboration, and leveraging cryptographic incentive mechanisms to ensure the honesty of contributions. The main challenges faced by this model include:

• Difficulty in Device Heterogeneity and Task Segmentation: High difficulty in coordinating heterogeneous devices, low efficiency in task segmentation.
• Communication efficiency bottleneck: Network communication is unstable, and gradient synchronization bottleneck is obvious.
• Lack of Trusted Execution: Lack of a trusted execution environment makes it difficult to verify whether nodes are truly participating in computation.
• Lack of unified coordination: no central scheduler, task distribution, and complex exception rollback mechanisms

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 aspects such as system architecture, communication protocols, cryptographic security, economic mechanisms, and model validation. Whether it can achieve "collaborative effectiveness + incentivize 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 scenarios that prioritize privacy compliance. Federated learning possesses the engineering structure of distributed training and local collaboration capabilities, while also enjoying the data dispersion advantages of Decentralization training, but 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 compliance scenarios, relatively mild in terms of training tasks, trust structures, and communication mechanisms, making it more suitable as a transitional deployment architecture in the industrial sector.

The Boundaries, Opportunities, and Realistic 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 complexity of the task structure, extremely high resource requirements, 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-speed bandwidth, making it difficult 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; while tasks lacking collaborative incentive foundations miss external participation motivation. These boundaries collectively constitute the current realistic limitations of Decentralization training.

However, this does not mean that decentralized training is a pseudoproposition. In fact, in lightweight, easily parallelizable, and incentivized task types, decentralized 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, resource-controllable small foundation model training, and collaborative training scenarios involving edge devices. These tasks generally exhibit 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, in the forefront of the fields of Decentralization training and federated learning, representative blockchain projects mainly include Prime Intellect, Pluralis.ai, Gensyn, Nous Research, and Flock.io. From the perspective 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.

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 three major modules: PRIME-RL, TOPLOC, and SHARDCAST.

Prime Intellect Protocol Stack Structure and Key Module Value

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 customized by Prime Intellect for decentralized training scenarios, specifically designed for heterogeneous networks and asynchronous participation. It employs reinforcement learning as the preferred adaptation object, structurally decoupling the training, inference, and weight uploading processes, allowing each training node to independently complete task loops locally and collaborate through standardized interfaces with verification 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 while laying the groundwork for supporting multi-task parallelism and strategy evolution.

#TOPLOC: Lightweight Training Behavior Verification Mechanism

TOPLOC 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 solutions such as ZKML, TOPLOC does not rely on full model recomputation but completes lightweight structural verification by analyzing the local consistency trajectory between "observation sequence ↔ policy update". It transforms the behavioral trajectory during the training process into a verifiable object for the first time, which is a key innovation for achieving trust-free training reward distribution, providing a feasible path for constructing an auditable and incentivized Decentralization collaborative training network.

#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 gossip propagation mechanisms with local synchronization strategies, allowing multiple nodes to continuously submit partial updates while being in a desynchronized 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 decentralized training, serving as a 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 such as Ring, Expander, and Small-World to avoid the high communication overhead of global synchronization, relying only on local neighbor nodes to complete model collaborative training. By combining asynchronous updates with checkpoint fault tolerance mechanisms, OpenDiLoCo enables consumer-grade GPUs and edge devices to stably participate in training tasks, significantly enhancing the accessibility of global collaborative training, making it one of the key communication infrastructures for building decentralized training networks.

#PCCL: Collaborative Communication Library

PCCL is a lightweight communication library tailored by Prime Intellect for decentralized AI training environments, aiming to address 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 a fundamental component supporting the asynchronous communication capabilities of the OpenDiLoCo protocol. It significantly enhances the bandwidth tolerance and device compatibility of training networks, paving the way for building a truly open and trustless collaborative training network by solving the "last mile" communication infrastructure.

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 genuine contributions. The protocol operates based on three core roles:

• Task initiator: define training environment, initial model, reward function, and validation criteria
• Training Node: Execute local training, submit weight updates and observation traces
• Validator nodes: Use the TOPLOC mechanism to verify the authenticity of training behavior 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."

INTELLECT-2: The release of the first verifiable Decentralization training model

Prime Intellect launched INTELLECT-2 in May 2025, which is the world's first large-scale reinforcement learning model trained by asynchronous, trustless decentralized nodes, with a parameter scale of 32B. The INTELLECT-2 model was collaboratively trained using over 100 GPU heterogeneous nodes spread across three continents, employing a fully asynchronous architecture, with a training duration exceeding 400 hours, demonstrating the feasibility and stability of asynchronous collaborative networks. This model is not only a breakthrough in performance but also 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), marking the first realization of an open, verifiable, and economically incentivized closed loop in decentralized training networks.

In terms of performance, INTELLECT-2