The Holy Grail of Crypto AI: Pioneering Exploration of Decentralization Training

In the entire value chain of AI, model training is the most resource-intensive and technically demanding phase, directly determining the upper limit of the model's capabilities and the effectiveness of its practical applications. Compared to the lightweight calls of 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 the entire training process through a unified control system, from hardware, underlying software, cluster scheduling systems, to all components of the training framework. 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 computation and storage. Although it physically possesses "distributed" characteristics, it is still overall controlled and scheduled by a centralized organization, often operating in a high-speed local area network environment. Through NVLink high-speed interconnect bus technology, the main node unifies and coordinates the sub-tasks. 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: Refined 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 in this way.

Decentralization training represents a future path with greater openness and anti-censorship characteristics. Its core features include: multiple untrusted nodes (, which may be personal computers, cloud GPUs, or edge devices ), collaborating to complete training tasks without a central coordinator, typically driving task distribution and collaboration through protocols, and ensuring the honesty of contributions through cryptographic incentive mechanisms. 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: The absence 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, task distribution, and complex 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 aspects such as system architecture, communication protocols, cryptographic security, economic mechanisms, and model validation. Whether it can achieve "collaborative efficiency + 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, making it suitable for privacy-compliant scenarios such as healthcare and finance. Federated learning possesses the engineering structure of distributed training and local collaborative capabilities while also having the data distribution advantages of Decentralization training, but it still relies on trusted coordinating parties and does not have completely open and censorship-resistant characteristics. It can be seen as a "controlled Decentralization" solution in privacy-compliant scenarios, relatively mild in terms of 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, 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 an open network; tasks with strong data privacy and sovereignty restrictions, such as medical, financial, and sensitive data (, are constrained by legal compliance and ethical constraints, 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 together constitute the current practical limitations of Decentralization training.

But this does not mean that decentralized training is a false proposition. In fact, decentralized training shows clear application prospects in lightweight, easily parallelizable, and incentivized task types. 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, and collaborative training scenarios involving edge devices. These tasks generally have 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, representative blockchain projects in the forefront of decentralized training and federated learning 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 architecture behind these five projects, and further explore their differences and complementary relationships in the decentralized AI training system.

( Prime Intellect: A pioneer of verifiable training trajectory 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 create a verifiable, open, and fully incentivized AI Decentralization training system through three major modules: PRIME-RL, TOPLOC, and SHARDCAST.

)# Detailed Explanation of Core Technology 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 employs reinforcement learning as a priority adaptation object, structurally decoupling the training, inference, and weight uploading processes, allowing each training node to independently complete the task loop locally and collaborate with verification 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 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 truly completed effective policy learning based on observational data. Unlike heavyweight solutions such as ZKML, TOPLOC does not rely on full model recomputation but instead completes lightweight structural verification by analyzing the local consistency trajectories between "observation sequence ↔ policy update." It is the first to transform the behavioral trajectories during the training process into verifiable objects, 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 while in asynchronous states, achieving gradual 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 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 encountered in decentralized training, such as bandwidth limitations, device heterogeneity, and node instability. 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 model collaborative training. By integrating asynchronous updates and checkpoint fault tolerance mechanisms, OpenDiLoCo allows consumer-grade GPUs and edge devices to stably participate in training tasks, significantly enhancing the accessibility of global collaborative training. It is one of the key communication infrastructures for building decentralized training networks.

PCCL: Collaborative Communication Library

PCCL 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 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 capability 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, trustless collaborative training network, overcoming the "last mile" of 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 receive 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 protocol includes task release, node training, trajectory verification, weight aggregation ) SHARDCAST ### and reward distribution, forming an incentive closed loop around "real training behavior".

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(# 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 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 spread 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 marks 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 framework(, TOPLOC) training behavior verification###, and SHARDCAST( asynchronous weight aggregation), signifying that the decentralized training network has achieved the openness and verification of the training process for the first time.