The Evolution of AI Training Paradigms: From Centralized Control to a Technological Revolution of Decentralized Collaboration

In the entire value chain of AI, model training is the stage with the highest resource consumption and the highest technical threshold, directly determining the model's capability ceiling and practical application effects. Compared to the lightweight calls in 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 the perspective of architectural paradigms, 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, where a single organization completes the entire training process within a local high-performance cluster. All components, from hardware, underlying software, cluster scheduling systems, to training frameworks, are coordinated by a unified control system. 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 faces issues such as data monopoly, 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 bottlenecks of single-machine computing and storage. Although it physically possesses "Decentralization" characteristics, it is still controlled and scheduled by a centralized institution, often operating in high-speed local area network environments. Through NVLink high-speed interconnect bus technology, the main node coordinates various sub-tasks uniformly. Mainstream methods include:

• Data parallelism: each node trains on different data while sharing parameters, requiring model weights to match.
• Model parallelism: Deploy different parts of the model on different nodes to achieve strong scalability;
• Pipeline Parallelism: Staged Serial Execution, Improving 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 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 anti-censorship characteristics. Its core feature lies in: multiple mutually distrustful nodes collaboratively completing training tasks without a central coordinator, usually driven by protocols for task distribution and collaboration, and ensuring the honesty of contributions through cryptographic incentive mechanisms. The main challenges faced by this model include:

• Heterogeneous devices and partitioning difficulties: High difficulty in coordinating heterogeneous devices and low efficiency in task partitioning;
• Communication efficiency bottleneck: Network communication is unstable, and 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 computation;
• Lack of unified coordination: No central scheduler, 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, but "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. However, whether it can achieve "effective collaboration + incentive for 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 features the engineering structure of distributed training and local collaboration capabilities, while also benefiting from 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 compliance scenarios, with relatively moderate training tasks, trust structures, and communication mechanisms, making it more suitable as a transitional deployment architecture in the industry.

Decentralization training's boundaries, opportunities, and realistic paths

From the perspective of training paradigms, Decentralization training is not suitable for all types of tasks. In certain scenarios, due to the complexity of task structures, extremely high resource requirements, or difficulties in collaboration, 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, making open sharing impossible; while tasks lacking a basis for collaborative incentives lack external participation motivation. These boundaries collectively constitute the current practical limitations of Decentralization training.

However, this does not mean that decentralized training is a false proposition. In fact, in lightweight structures, easily parallelizable, and incentivizable task types, decentralized training shows clear application prospects. This includes, but is not limited to: LoRA fine-tuning, behavior alignment post-training tasks, data crowdsourced training and labeling tasks, resource-controllable small foundational model training, 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.

Decentralization training classic project analysis

Currently, in the forefront of decentralized training and federated learning, the 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 the implementation paths of Gensyn and Flock.io are relatively clear, and initial engineering progress can be seen. This article will successively analyze the core technologies and engineering architecture behind these five projects, and further discuss their differences and complementary relationships in the decentralized AI training system.

Prime Intellect: A pioneer in verifiable training trajectories for reinforcement learning collaborative networks

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

1. Prime Intellect Protocol Stack Structure and Key Module Value

2. 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, designed specifically for heterogeneous networks and asynchronous participation. It uses 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 strategy 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 genuinely completed effective policy learning based on observational data. Unlike heavyweight solutions like ZKML, TOPLOC does not rely on full model recomputation, but rather completes lightweight structural verification by analyzing the local consistency trajectories between "observation sequence ↔ policy update." It transforms the behavioral trajectories during the training process into verifiable objects for the first time, which is a key innovation for achieving trustless training reward distribution, providing a feasible path for building 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 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 decentralized training, serving as a core foundation for building stable weight consensus and continuous training iteration.

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, heterogeneous devices, and unstable nodes. Its architecture is based on data parallelism, building 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 collaborative model 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 participatory nature 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 decentralized AI training environments by Prime Intellect, aimed at addressing the adaptation bottlenecks of traditional communication libraries in heterogeneous devices and low-bandwidth networks. PCCL supports sparse topologies, 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 capability 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 bridging the "last mile" of communication infrastructure.

3. Prime Intellect Incentive Network and Role Division

Prime Intellect has built a permissionless, verifiable, and economically incentivized training network, allowing anyone to participate in tasks and earn rewards based on genuine 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 behavior, and participate in reward calculation and strategy aggregation.

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

4. 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 by asynchronous, trustless decentralized node collaboration, with a parameter scale of 32B. The INTELLECT-2 model was collaboratively trained by over 100 GPU heterogeneous nodes spread across three continents, using a fully asynchronous architecture, with a training duration exceeding 400 hours, demonstrating the feasibility and stability of an asynchronous collaborative network. This model not only represents a breakthrough in performance but also marks the first systematic implementation of the "training as consensus" paradigm proposed by Prime Intellect. INTELLECT-2 integrates core protocol modules such as PRIME-RL, TOPLOC, and SHARDCAST, signifying decentralization in training.