The Holy Grail of Crypto AI: Cutting-edge Exploration of Decentralization Training

In the full value chain of AI, model training is the most resource-consuming and technically challenging phase, directly determining the upper limit of the model's capabilities and its actual 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 an architectural paradigm perspective, training methods can be divided into four categories: centralized training, distributed training, federated learning, and the focus of this article, Decentralization training.

Centralized training is the most common traditional method, completed by a single institution within a local high-performance cluster, coordinating all training processes from hardware, underlying software, cluster scheduling systems, to all components of the training framework through 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 such as 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 approach 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 overcome the computing and storage bottlenecks of a single machine. Although it possesses "distributed" characteristics physically, the overall process is still controlled and scheduled by a centralized entity, often operating in a high-speed local area network environment, and utilizing NVLink high-speed interconnect bus technology, where the main node coordinates all sub-tasks uniformly. Mainstream methods include:

• Data parallelism: Each node trains on different data while sharing 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 completing tasks. Currently, almost all mainstream large models are trained in this way.

Decentralization training represents a future path that is more open and resistant to censorship. Its core characteristics are: multiple untrusted nodes collaboratively completing training tasks without a central coordinator, typically driven by protocols for task distribution and collaboration, and relying on cryptographic incentive mechanisms to ensure the integrity of contributions. The main challenges faced by this model include:

• Device heterogeneity and segmentation difficulties: High difficulty in coordinating heterogeneous devices, low efficiency in task segmentation.
• Communication efficiency bottleneck: Network communication is unstable, and the gradient synchronization bottleneck is obvious.
• Lack of Trusted Execution: The absence 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, but "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. However, whether it can achieve "effective collaboration + honest incentives + 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 has the engineering structure of distributed training and local collaboration capabilities, while also possessing the data dispersion advantages of Decentralization training. However, it still relies on trusted coordinating parties and does not have the characteristics of being completely open and resistant to censorship. It can be seen as a "controlled Decentralization" solution in privacy-compliant scenarios, which is relatively moderate in terms of training tasks, trust structures, and communication mechanisms, making it more suitable as a transitional deployment architecture for 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, 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 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; and tasks lacking a foundation of collaborative incentives lack external participation motivation. These boundaries together constitute the current realistic limitations of Decentralization training.

However, this does not mean that decentralized training is a false proposition. In fact, in lightweight, easily parallelizable, and incentivized task types, decentralized training shows clear application prospects. This includes but is not limited to: LoRA fine-tuning, behavior alignment post-training tasks, 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 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 initial engineering progress can be observed. 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 Decentralized AI training system.

Prime Intellect: Verifiable Training Trajectories for Reinforcement Learning Collaborative Networks Pioneer

Prime Intellect is committed to building a trustless AI training network, enabling anyone to participate in training and receive reliable 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

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 customized by Prime Intellect for decentralized training scenarios, specifically designed for heterogeneous networks and asynchronous participation. It adopts reinforcement learning as the preferred adaptation object, structurally decoupling the training, inference, and weight uploading processes, allowing each training node to independently complete the task cycle 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 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 instead completes lightweight structural verification by analyzing the local consistency trajectories between "observation sequences ↔ policy updates." It transforms the behavioral trajectories during the training process into verifiable objects for the first time, representing 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 Weighted 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 while being out of sync, 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 common challenges in decentralized training, such as bandwidth limitations, device heterogeneity, and unstable nodes. Its architecture is based on data parallelism and constructs 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 coordinated 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 participatory nature 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 a Decentralization AI training environment, 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, 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, bridging the "last mile" of communication infrastructure for building a truly open and trustless collaborative training network.

03, Prime Intellect Incentive Network and Role Division

Prime Intellect has built a permissionless, verifiable, and economically incentivized training network that allows 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
• 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."

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 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 distributed across three continents, using a fully asynchronous architecture, with a training duration exceeding 400 hours, demonstrating the feasibility and stability of asynchronous cooperative 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 PRIME-RL, TOPLOC, and SHARDCAST, signifying that decentralized training networks have achieved training for the first time.