Evolution of AI Training Paradigms: From Centralized Control to Decentralization Collaboration in Technological Revolution

In the entire value chain of AI, model training is the most resource-intensive and technically challenging phase, directly determining the upper limit of the model's capabilities and its practical application effects. Compared to the lightweight invocation 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 the architectural paradigm perspective, training methods can be divided into four categories: centralized training, distributed training, federated learning, and the decentralized training discussed in this article.

Centralized training is the most common traditional method, carried out by a single institution within a local high-performance cluster, completing the entire training process. From hardware ( such as NVIDIA GPUs ), underlying software ( like CUDA, cuDNN ), cluster scheduling systems ( like Kubernetes ), to training frameworks ( such as PyTorch ) based on the NCCL backend, all components 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 approach for training large models currently. Its core is to decompose the model training tasks and distribute them to multiple machines for collaborative execution, in order to overcome the bottlenecks of single-machine computing and storage. Although it physically has "distributed" characteristics, it is still controlled, scheduled, and synchronized by centralized organizations, often operating in high-speed local area network environments, coordinating various sub-tasks through NVLink high-speed interconnect bus technology by a master node. Mainstream methods include:

• Data parallelism: each node trains on different data while sharing parameters, model weights need to match.
• 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 segmentation of matrix computations 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 tasks. Currently, almost all mainstream large models such as (GPT-4, Gemini, LLaMA, etc., are trained in this way.

Decentralization training represents a future path with greater openness and anti-censorship characteristics. Its core features include: multiple trustless nodes ) that could be home computers, cloud GPUs, or edge devices ( 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:

• Device heterogeneity and partitioning difficulties: High coordination difficulty of heterogeneous devices, low task partitioning efficiency.
• 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 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 a model. 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 + incentive 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, suitable for privacy-compliance scenarios such as healthcare and finance ). Federated learning has the engineering structure of distributed training and local collaboration capabilities, while also possessing the advantages of decentralized training through data distribution. However, it still relies on trusted coordinators and does not have the characteristics of complete openness and resistance to censorship. It can be seen as a "controlled Decentralization" solution in privacy-compliance scenarios, relatively moderate in training tasks, trust structures, and communication mechanisms, making it more suitable as a transitional deployment architecture for the industry.

( AI Training Paradigm Comprehensive Comparison Table ) Technical Architecture × Trust Incentive × Application Features ###

( Decentralization training: 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 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 partition and synchronize in open networks; tasks with strong data privacy and sovereignty restrictions such as medical, financial, and sensitive data ) are constrained by legal compliance and ethical limitations, making open sharing impossible; while tasks lacking collaborative incentive foundations such as enterprise closed-source models or internal prototype training ### 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, in task types that are lightweight in structure, easily parallelizable, and incentivizable, 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 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 Task Adaptability Overview

( Decentralization Training Classic Project Analysis

Currently, in the frontier fields of Decentralization training and federated learning, the 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 forefront direction of current theoretical research; while Gensyn and Flock.io have relatively clear implementation paths, and preliminary engineering progress can be observed. This article will sequentially analyze the core technologies and engineering architectures behind these five projects, and further discuss 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: PRIME-RL, TOPLOC, and SHARDCAST.

1. Prime Intellect Protocol Stack Structure and Key Module Value

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II. Detailed Explanation of the Prime Intellect Training Key Mechanism

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 employs reinforcement learning as the primary adaptation object, structurally decoupling the training, inference, and weight uploading 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 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)Trusted Observation & Policy-Locality Check### is a core mechanism for training verifiability 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 rather 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, representing a key innovation for achieving trustless training reward distribution, and provides 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 characterized by asynchrony, bandwidth constraints, and variable node states. It combines a gossip propagation mechanism with local synchronization strategies, allowing multiple nodes to continuously submit partial updates in an out-of-sync 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 specifically designed to address challenges commonly faced in decentralized training, such as bandwidth constraints, device heterogeneity, and unstable nodes. 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 collaborative model training. By combining asynchronous updates and fault tolerance mechanisms, OpenDiLoCo enables consumer-grade GPUs and edge devices to stably participate in training tasks, significantly enhancing the participability 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 for the decentralized AI training environment by Prime Intellect, aimed at addressing the adaptation bottlenecks of traditional communication libraries ) such as NCCL and Gloo( 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. It is the underlying 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 bridging the "last mile" of communication infrastructure.

Three, 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 training environment, initial model, reward function, and validation criteria
• Training Node: Execute local training, submit weight updates and observation trajectories
• Verification 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".

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4. INTELLECT-2: The Release of the First Verifiable Decentralization Training Model

Prime Intellect launched INTELLECT-2 in May 2025, which is a full