BitTorrent推出BTTInferGrid构筑去中心化AI推理算力底座，有望赋能BTT价值全面跃升

On June 17, BitTorrent, the world's leading decentralized file transfer ecosystem, officially launched its core AI strategic product, BTTInferGrid, building a decentralized computing network for AI inference scenarios.

BTTInferGrid is a major AI product strategically upgraded by BitTorrent based on its mature decentralized storage service, BTFS. It embodies BitTorrent's deep technical expertise accumulated over the years in core areas such as P2P network protocol design, global distributed node governance, and large-scale resource scheduling. This gives the platform inherent advantages for large-scale application and commercial implementation from its inception. The official launch of this product not only marks BitTorrent's entry into the decentralized AI infrastructure赛道 but also officially opens a new chapter where distributed computing power empowers the development of the AI industry.

Relying on a cryptoeconomic incentive system and a distributed consensus mechanism, BTTInferGrid seamlessly connects globally idle GPU computing resources with the diverse inference needs of AI developers. It provides next-generation AI applications with open, verifiable, pay-as-you-go, and efficient inference services, while also allowing idle GPU holders to easily monetize their assets, creating a win-win scenario for both computing supply and demand.

From a technical infrastructure perspective, BTTInferGrid reconstructs the traditional centralized computing supply system through distributed computing aggregation and intelligent scheduling mechanisms, granting AI infrastructure greater resource elasticity and resilience against risks. From an industry landscape perspective, it helps computing power break free from scarcity and monopoly, transforming it into a freely circulating digital means of production. This enables every GPU holder to participate in value co-creation and profit distribution, fostering a new industry paradigm of universal access, sharing, and efficient circulation of computing resources.

BitTorrent Launches BTTInferGrid to Build a Decentralized AI Inference Computing Base

"Computing power, algorithms, and data" are the three core elements of AI development, and the strategic value of computing power has been elevated to an unprecedented level in 2026. The "computing power shortage" is no longer just a long-term industry warning but has evolved into the primary bottleneck hindering AI progress.

Globally, the rental cost for high-end NVIDIA GPUs continues to rise, and hardware supply remains persistently tight. Leading AI companies like OpenAI and Anthropic frequently experience server outages due to insufficient computing reserves. Even tech giants and top academic institutions struggle to secure adequate computing power. In its recent Nasdaq IPO prospectus, SpaceX acknowledged that its AI system's computing needs significantly exceed current market supply, even considering reclaiming computing resources previously lent to Anthropic. Recent reports also revealed that Microsoft's cloud platform, Azure, urgently requested computing capacity from rival Amazon AWS to cope with the massive surge in code submissions from GitHub in the AI era. Meanwhile, AI labs at top universities like Stanford and MIT have had to suspend multiple large model training projects due to insufficient computing power, causing delays in graduate thesis defenses.

It is against this backdrop of intensifying global supply-demand矛盾 for computing power that BTTInferGrid was created. It aims to build a decentralized AI inference computing network (DePIN) by aggregating scattered idle GPU resources worldwide in a decentralized manner. It precisely matches the business needs of AI developers, breaks down the barriers and monopolies formed by traditional centralized computing service providers, maximizes the utilization of global idle hardware resources, and establishes a new generation of inclusive, open, and shared underlying computing infrastructure. This fully unleashes the potential of global idle hardware resources, ensuring every unit of computing power is utilized and maximizing its value.

To ensure the efficient implementation of the entire operational system, BTTInferGrid adopts a modular, layered architecture design, establishing a three-tier collaborative system consisting of the "Application Layer — Computing Layer — Settlement Layer":

• Application Layer: Serving as the service entry point for developers, the Application Layer provides a friendly deployment environment, supporting the rapid implementation of various AI-native applications, such as AI chatbots, intelligent agents, and other diverse scenarios.
• Computing Layer: As the core computing hub of the entire ecosystem, the Computing Layer is responsible for key tasks including AI model inference operations, real-time request responses, and task scheduling.
• Settlement Layer: The Settlement Layer manages the automated operation of the entire economic system, covering the full process of computing staking, task settlement, contribution reward distribution, and malicious node penalties. This layer executes on-chain transactions in a trustless manner, ensuring fair and transparent value exchange between computing supply and demand without intermediaries, providing a solid foundation of economic trust for the entire network.

The three tiers collaborate efficiently through standardized interfaces: the Application Layer initiates inference requests, the Computing Layer schedules computing resources for execution, and the Settlement Layer automatically distributes incentives based on the execution results. These three tiers support each other, forming a closed-loop system that collectively constitutes a high-performance, high-trust, and sustainable decentralized AI inference infrastructure.

Built on this three-tier architecture, BTTInferGrid offers multiple advantages, including distributed node autonomy, demand-driven permissionless access, and end-to-end trust and verifiability, creating an efficient, robust, and open distributed computing environment with no barriers to entry.

From a network architecture perspective, BTTInferGrid adopts a globally distributed node deployment strategy. All nodes are collectively owned and operated in a distributed manner by the community, with no single data center or operating entity controlling the network core. This inherently decentralized design completely avoids common risks like single points of failure and operational interruptions found in traditional centralized platforms, granting the network strong censorship resistance and 7×24 uninterrupted service resilience, providing a highly available operating base for various AI inference tasks.

Regarding the access and scheduling rules for computing resources, BTTInferGrid implements a permissionless open mechanism: all GPU devices meeting performance standards can freely join the network without central authority review. Furthermore, the overall computing supply is entirely driven by real business demand. Incentives are calculated based on the actual computing volume invoked from nodes and their comprehensive service performance, supplemented by a dynamic supply adjustment mechanism that flexibly allocates resource scale based on real-time network load. This mechanism enhances computing resource turnover efficiency while ensuring long-term, stable income for suppliers proportional to their contributions.

In terms of trust mechanisms, BTTInferGrid embeds trust logic throughout the entire business process. Leveraging a comprehensive crypto-economic system, the network automates operations like computing scheduling, task assignment, and revenue settlement. Every AI inference computation task can be traced end-to-end, and the results support on-chain cross-verification. Through this underlying mechanism design, the network eliminates malpractices such as false computing reporting and data tampering from the source, ensuring the authenticity and integrity of all computing tasks, allowing demand-side users to use with confidence and supply-side participants to engage securely.

In summary, the distributed node architecture endows the computing network with autonomy and high stability; the demand-driven, permissionless access model ensures efficient computing flow and long-term economic sustainability; and the fully verifiable trust system maintains the ecosystem's safety baseline. The deep integration of these three core features makes BTTInferGrid not just a technologically advanced distributed computing network but a long-term stable, highly trustworthy, and future-oriented decentralized AI infrastructure.

BTT Poised to Become Core Value Token of Decentralized AI Computing Network, Potentially Broadening Ecosystem Application Boundaries

As the native value token of the BitTorrent ecosystem, with the official launch and continuous expansion of the BTTInferGrid ecosystem, BTT's strategic positioning is poised for a critical upgrade. Its application scenarios are expected to extend from traditional distributed transmission and storage tracks to the entire industrial chain of AI computing infrastructure, continuously broadening its ecological value boundaries.

Previously, BTT was the circulation medium for BitTorrent, the world's leading decentralized file transfer network. Now, relying on the new AI computing network BTTInferGrid, it is expected to advance to become the core token for scheduling the decentralized AI computing network, assuming dual functions of value transfer and ecosystem governance.

The crypto-economic incentive mechanism of BTTInferGrid serves as the underlying engine for network operation. It connects idle off-chain GPU computing power with the inference needs of AI developers, automating task scheduling, result verification, and revenue settlement through token incentives, ensuring supply-demand matching and governance transparency.

Within the BTTInferGrid system, the continuous operation of the ecosystem relies mainly on the collaborative participation and division of labor among three core roles: Miners, Users (AI developers), and Verifiers, collectively building an autonomously operating decentralized computing network:

• Miners (Computing Supply): Contribute idle GPU resources, undertake and execute AI inference tasks, and earn corresponding rewards based on actual workload, task completion quality, and dynamic performance scores.
• AI Developers (Computing Demand): Can access the global distributed computing pool through a unified standardized API, significantly reducing computing costs.
• Verifiers (Network Guardians): Audit and randomly challenge the computing performance of miner nodes, identify abnormal behaviors like node cheating or low-quality computation, and earn rewards by maintaining network security and service quality.

These three types of participants form a complete closed loop of symbiotic interests and mutual constraints through a decentralized consensus mechanism, jointly driving the continuous evolution and virtuous cycle of the BTTInferGrid ecosystem. The core link connecting the interests of all parties and driving the healthy operation of the ecosystem is the crypto-economic incentive system tailor-made for BTTInferGrid.

Through the circulation of tokens, this system achieves precise quantification and fair distribution of computing value, transforming behaviors like computing supply, task execution, and result auditing into clear and quantifiable incentive signals: Miners receive token rewards for contributing idle GPUs and completing inference tasks with high quality; Verifiers earn revenue by maintaining network security; and AI developers pay fees based on actual computing consumption. The interests of the three parties achieve dynamic balance through the circulation of the token economy, thereby constructing a sustainable value loop.

Within this framework, BTT is expected to become the unified native incentive and settlement base token within the BTTInferGrid ecosystem, permeating the core links of the entire computing ecosystem. It will comprehensively cover the usage fees, contribution incentives, and dynamic allocation processes for AI computing resources, ultimately building a closed-loop economic system where "computing contributors are rewarded, computing users pay conveniently, and ecosystem participants share value."

Specifically, the BTT token can assume multiple core roles within the BTTInferGrid network: As a payment medium, AI developers use BTT (or its equivalent tokens) to pay for inference services, achieving "pay-as-you-go" procurement. As an incentive tool, Miners receive token rewards based on verified actual computing contributions, and Verifiers earn revenue for providing audit and challenge services, continuously attracting global idle resources to join the network. As a staking asset, Verifiers need to stake tokens to participate in scoring and verification, and computing nodes also need to stake a certain number of tokens to qualify for undertaking tasks. Any misconduct triggers a slashing mechanism, effectively ensuring network security and fairness from an economic standpoint.

From this perspective, BTT is not only expected to be the value vehicle matching computing supply and demand in the future but also the fundamental core driving force supporting the efficient, fair, and long-term operation of the entire decentralized AI computing economy. On one hand, token incentives continuously attract more idle GPU resources to join the network, expanding computing supply. On the other hand, the accompanying staking and slashing mechanism ensures the stability and reliability of inference services. Simultaneously, all settlement, reward, and penalty logic is automatically executed by smart contracts, effectively addressing common pain points in centralized computing platforms, such as information opacity and high trust costs.

As the BTTInferGrid ecosystem develops and flourishes, BTT is expected to become a universal value anchor connecting distributed computing power with AI application demands, opening a new paradigm for the decentralized AI economy.

BTTInferGrid Reshapes Global Computing Allocation Mechanism, BitTorrent Opens New Chapter in Decentralized AI Track

Against the industry backdrop of intensifying global AI computing supply-demand矛盾 and increasing centralized computing monopolies, BTTInferGrid reconstructs the computing supply model through distributed technology: it efficiently aggregates fragmented idle GPU resources worldwide, building an open and shared computing infrastructure. This allows AI developers to access elastic computing power with zero barriers while enabling every unit of idle computing power globally to realize its potential value. Simultaneously, leveraging an innovative crypto-economic incentive and collaborative governance mechanism, it opens up a value circulation loop between the computing supply side and the demand side, forming an ecological cycle of mutual promotion and healthy operation.

For Miners (Computing Supply), BTTInferGrid acts as a "value converter" that transforms idle computing power into sustainable income. Any idle GPU meeting basic performance thresholds can join the network without permission and contribute computing power to earn rewards.

Different from the rough model of traditional distributed computing platforms that allocate income simply based on "hardware computing power," BTTInferGrid adopts a multi-dimensional weighted scoring incentive model. The network comprehensively evaluates core indicators such as the node's actual effective workload, task response latency, service stability, and result accuracy, dynamically calculating and distributing corresponding rewards. This mechanism completely breaks the pattern where "large computing power monopolizes rewards," allowing small and medium-sized miners providing high-quality, reliable services to also obtain excess returns, thereby ensuring the service quality of the entire network institutionally. Furthermore, miners participating in the network's early construction will enjoy exclusive benefits like reward multipliers, gaining a first-mover advantage.

For AI Developers, BTTInferGrid offers open, verifiable, and flexible pay-as-you-go AI inference computing services—a computing solution entirely different from traditional cloud vendors. It effectively addresses multiple industry-wide pain points such as "expensive computing, poor elasticity, and trust difficulty," significantly lowering the trial-and-error barriers for deploying AI applications.

Firstly, BTTInferGrid provides elastic computing scheduling, dynamically allocating resources based on AI inference load. Developers do not need to pre-purchase hardware or sign long-term contracts, completely freeing themselves from resource lock-in by centralized cloud providers, enabling true on-demand access and flexible scaling. Secondly, it adopts a decentralized market-driven pricing and precise token-based billing model, eliminating the high premiums of centralized platforms and significantly reducing inference costs, bringing computing expenditure back to reasonable levels. Crucially, BTTInferGrid builds a decentralized multi-verifier audit network. Through multiple mechanisms like random challenges, cross-verification, and staking penalties, it technically prevents computing fraud and result tampering, ensuring every inference computation is authentic, traceable, and verifiable. These multiple advantages complement each other, making BTTInferGrid not only a cost-effective channel for accessing computing power but also a trustworthy decentralized AI inference infrastructure for developers.

In terms of product development, BTTInferGrid has formulated clear, actionable short-term, medium-term, and long-term development plans to steadily advance the iterative upgrade and ecological expansion of the decentralized AI computing network:

Short-term Goal (2026): Focus on network launch and basic service deployment. While gradually increasing the number of online GPU nodes, complete core node deployment and inference service verification. Add support for mainstream open-source models like DeepSeek and Qwen. Launch API services for developers and enterprise clients.

Medium-term Goal (2027): Focus on ecological closure and capability boundary expansion. Based on stable inference service operation, comprehensively enhance network performance and ecological richness. Achieve an upgrade from a single inference service to a comprehensive computing platform (e.g., model fine-tuning, cross-chain resource access). Build a complete developer toolchain and ecosystem support system.

Long-term Goal (2028 and beyond): Strive to become an AI-native infrastructure, creating a collaborative network integrating computation, storage, and smart contracts. Provide underlying support for AI agents and automated applications. Ultimately become the preferred decentralized inference layer for global open-source AI applications, providing elastic, inclusive, and trustworthy computing support for large-scale, high-concurrency next-generation AI application scenarios.

In terms of ecosystem development, BTTInferGrid has already completed native adaptation for several top-tier industry open-source large models. This includes mainstream models like Alibaba Cloud's Tongyi Qianwen Qwen3.6 27B, Qwen2.5 7B Instruct, and Meta's Llama 3.1 8B Instruct, covering diverse business scenarios such as general conversation, code generation, and content creation. Developers do not need to deploy and debug models themselves; they can flexibly invoke them on demand through standardized API interfaces, further lowering the usage barrier and significantly shortening the AI application development and deployment cycle.

Currently, users can submit miner access applications through the BTTInferGrid official website to participate early in network construction and share in ecosystem development dividends.