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

In the full value chain of AI, model training is the most resource-intensive and technically demanding stage, directly determining the upper limit of the model's capabilities and its actual application effects. Compared to the lightweight calls in the inference stage, 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 that this article focuses on.

Centralized training is the most common traditional method, where a single institution completes the entire training process within a local high-performance cluster, coordinating all components from hardware, underlying software, cluster scheduling systems, to training frameworks through a unified control system. This deeply coordinated architecture optimizes the efficiency of memory sharing, gradient synchronization, and fault tolerance mechanisms. It is very suitable for training large-scale models such as GPT and Gemini, offering advantages of high efficiency and controllable resources. However, it also presents issues such as data monopoly, resource barriers, energy consumption, and single-point risks.

Distributed training is the mainstream method for training large models. Its core is to decompose the model training tasks and distribute them to multiple machines for collaborative execution, in order to break through the computational and storage bottlenecks of a single machine. Although it physically possesses "distributed" characteristics, it is still controlled, scheduled, and synchronized by a centralized institution, often operating in a high-speed local area network environment, using NVLink high-speed interconnect bus technology, with the main node coordinating various sub-tasks. Mainstream methods include:

• Data parallelism: each node trains 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 tasks. Currently, almost all mainstream large models ( GPT-4, Gemini, LLaMA, etc. ) are trained in this way.

Decentralization training represents a more open and censorship-resistant future path. Its core features include: multiple untrusted nodes ( that could be home computers, cloud GPUs, or edge devices ) collaboratively completing 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 segmentation difficulties: High coordination difficulty of heterogeneous devices, low task segmentation 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 truly participating in the computation.
• Lack of unified coordination: no central dispatcher, 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. 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. Yet, 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 distribution and Decentralization, emphasizes local data retention and centralized aggregation of model parameters, making it suitable for privacy-compliant scenarios such as healthcare, finance, and (. Federated learning possesses the engineering structure of distributed training and local collaborative capabilities, while also having the data dispersion advantages of Decentralized training. However, it still relies on trusted coordinating parties and does not have the characteristics of being fully open and resistant to censorship. It can be seen as a "controlled Decentralization" solution in privacy-compliant scenarios, relatively moderate in training tasks, trust structures, and communication mechanisms, making it more suitable as a transitional deployment architecture in the industry.

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The Boundaries, Opportunities, and Realistic Paths of Decentralization Training

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 requirements, or difficulties in collaboration, it is inherently unsuitable for efficient completion between heterogeneous, trustless nodes. For example, large model training often relies on high memory, low latency, and high bandwidth, making it difficult to effectively split 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 and cannot be openly shared; while tasks lacking a collaborative incentive basis, such as enterprise closed-source models or internal prototype training (, 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, decentralized training demonstrates clear application prospects in tasks that are structurally lightweight, easily parallelized, and incentivized. 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 have high parallelism, low coupling, and can tolerate heterogeneous computing power, making them very suitable for collaborative training through P2P networks, Swarm protocols, distributed optimizers, and other methods.

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Decentralization Training Classic Project Analysis

Currently, in the forefront of decentralized training and federated learning, representative blockchain projects mainly include Prime Intellect, Pluralis.ai, Gensyn, Nous Research, and Flock.io. In terms of technical 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 of current theoretical research; while Gensyn and Flock.io have relatively clear implementation paths, and initial engineering progress can 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: Training trajectory verifiable reinforcement learning collaborative network pioneer

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

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02. 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, 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 task loops locally and collaborate with verification and aggregation mechanisms through standardized interfaces. Compared to traditional supervised learning processes, PRIME-RL is more suitable for achieving elastic training in environments without centralized scheduling, thereby reducing system complexity and laying the foundation for supporting multi-task parallelism and strategy evolution.

TOPLOC: Lightweight Training Behavior Verification Mechanism

TOPLOC(Trusted Observation & Policy-Locality Check) is a core mechanism of verifiable training 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 re-computation, but 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, which is a key innovation for achieving trustless training reward allocation, 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 gossip propagation mechanisms with local synchronization strategies, 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 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 designed specifically to address challenges commonly encountered 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 model collaborative training. Combined with asynchronous updates and checkpoint fault tolerance mechanisms, OpenDiLoCo allows 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###Prime Collective Communication Library( is a lightweight communication library tailored for a decentralized AI training environment developed by Prime Intellect, aimed at addressing the adaptation bottlenecks of traditional communication libraries like NCCL and Gloo) 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 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 overcoming 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: defines 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 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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(# 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 decentralized