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

In the AI full value chain, model training is the most resource-intensive and technically demanding phase, directly determining the upper limit of the model's capabilities and its 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 discussed in this article.

Centralized training is the most common traditional method, where a single institution completes the entire training process within a local high-performance cluster, with all components from hardware, underlying software, cluster scheduling systems, to training frameworks 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 monopoly, resource barriers, energy consumption, and single-point risks.

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

• Data parallelism: Each node trains on 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 segmentation of matrix computations 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 tasks. Currently, almost all mainstream large models are trained using this method.

Decentralization training represents a future path that is more open and resistant to censorship. Its core characteristics are: multiple untrusted nodes (, which may be home computers, cloud GPUs, or edge devices ), collaborating to complete training tasks without a central coordinator, usually 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:

• Heterogeneous devices and partitioning difficulties: High coordination difficulty for heterogeneous devices, low task partitioning efficiency.
• Communication efficiency bottleneck: Network communication is unstable, and the gradient synchronization bottleneck is obvious.
• Lack of 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 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 systematic engineering challenge, involving multiple aspects such as system architecture, communication protocols, cryptographic security, economic mechanisms, and model validation. Whether it can achieve "collaborative effectiveness + honest incentives + correct results" is still in the early prototype exploration stage.

Federated learning, as a transitional form between distribution and Decentralization, emphasizes local data retention and centralized aggregation of model parameters. It is suitable for privacy-compliant scenarios such as healthcare and finance. Federated learning has the engineering structure of distributed training and local collaboration capabilities, while also possessing the data dispersion advantages of Decentralized training. 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-compliant scenarios, relatively mild in 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 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, high resource demands, 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 bandwidth, making it difficult to effectively partition and synchronize in an open network; tasks with strong data privacy and sovereignty constraints are limited by legal compliance and ethical restrictions, making open sharing impossible; while tasks lacking a collaborative incentive foundation lack external participation motivation. These boundaries together constitute the current practical limitations of decentralization training.

However, this does not mean that decentralization training is a pseudo proposition. In fact, in task types that are lightweight in structure, easy to parallelize, and incentivizable, decentralization training shows clear application prospects. This includes but is 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, as well as 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 methods such as P2P networks, Swarm protocols, and distributed optimizers.

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, and preliminary engineering progress can already be seen. This article will successively 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: PRIME-RL, TOPLOC, and SHARDCAST.

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

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

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

#PRIME-RL: Decoupled Asynchronous Reinforcement Learning Task Architecture

PRIME-RL is a task modeling and execution framework custom-built by Prime Intellect for decentralized training scenarios, 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 more suitable for achieving 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) 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 like 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 verifiable objects for the first time, which is a key innovation for achieving trustless 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 a gossip propagation mechanism with local synchronization strategies, allowing multiple nodes to continuously submit partial updates in an asynchronous 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 a 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 proposed by DeepMind. It is specifically designed 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 topological structures like Ring, Expander, and Small-World to avoid the high communication overhead of global synchronization, completing model collaborative training relying only on local neighbor nodes. By combining asynchronous updates and a fault tolerance mechanism, OpenDiLoCo allows 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(Prime Collective Communication Library) is a lightweight communication library tailored by Prime Intellect for a Decentralization 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 supporting 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, thus solving the "last mile" communication foundation.

(# 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 behavior and participate in reward calculation and strategy aggregation.

The core process of the agreement 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 through asynchronous, trustless decentralized node collaboration, with a parameter scale of 32B. The INTELLECT-2 model was collaboratively trained by over 100 GPU heterogeneous nodes 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 signifies Prime Intellect.