BitTorrent launches BTTInferGrid to build a decentralized AI inference computing foundation, which is expected to empower a comprehensive leap in BTT's value

On June 17, the globally leading decentralized file transfer ecosystem BitTorrent announced the launch of its core AI strategic product BTTInferGrid, building a decentralized computing power network aimed at AI inference scenarios.

BTTInferGrid is a heavyweight AI product strategically upgraded by BitTorrent based on its mature decentralized storage service BTFS. It consolidates BitTorrent's deep technical experience accumulated over the years in core areas such as P2P network protocol design, global distributed node governance, and large-scale resource scheduling, giving the platform an inherent advantage for scalable applications and commercial implementation from the outset. The official launch of this product marks not only the beginning of BitTorrent's layout in the decentralized AI infrastructure track but also the start of a new chapter in empowering the AI industry with distributed computing power.

Relying on a cryptoeconomic incentive system and a distributed consensus mechanism, BTTInferGrid seamlessly connects global idle GPU computing power resources with the diverse inference needs of AI developers, providing efficient inference services that are open access, verifiable, and pay-as-you-go for next-generation AI applications, while allowing idle GPU holders to easily monetize their resources, forming a win-win situation for both supply and demand of computing power.

From a technical perspective, BTTInferGrid reconstructs the traditional centralized computing power supply system through distributed computing power aggregation and intelligent scheduling mechanisms, endowing AI infrastructure with stronger resource elasticity and risk resilience. From an industrial perspective, it promotes the liberation of computing power from scarcity and monopoly attributes, transforming it into freely circulating digital production materials, enabling every GPU holder to participate in value co-creation and revenue distribution, achieving a new industrial pattern of inclusive sharing and efficient circulation of computing power resources.

BitTorrent Launches BTTInferGrid to Build a Decentralized AI Inference Computing Power Foundation

"Computing power, algorithms, and data" are the three core elements of AI development, and the strategic value of computing power is expected to reach unprecedented heights by 2026. The "computing power shortage" is no longer just a long-term warning for the industry but has evolved into the number one bottleneck restricting AI advancement.

Looking at the global market, the rental prices of high-end NVIDIA GPUs continue to rise, and hardware supply remains tight for a long time; leading AI companies like OpenAI and Anthropic often face server downtime due to insufficient computing power reserves; even tech giants and top academic institutions are scrambling to secure adequate computing power. Recently, SpaceX, which recently went public on NASDAQ, admitted in its IPO prospectus that its computing power needs for supporting AI systems have significantly exceeded the existing market supply, even considering reclaiming computing power resources previously leased to Anthropic for self-preservation. Recently, Microsoft's cloud platform Azure was also reported to have urgently sought assistance from competitor Amazon AWS to rent computing power to address the huge computing power gap caused by the surge in code submissions on GitHub in the AI era. Meanwhile, AI laboratories at top universities like Stanford and MIT have also suspended several large model training projects due to insufficient computing power, with many graduate thesis defenses being forced to postpone.

It is against the backdrop of increasingly intensified global supply and demand contradictions for computing power that BTTInferGrid was born. It aims to build a decentralized AI inference computing power network (DePIN) by aggregating scattered idle GPU computing power resources globally in a decentralized manner, accurately matching the business needs of a wide range of AI developers, breaking down barriers and monopolies formed by traditional centralized computing power service providers, maximizing the activation of global idle hardware resources, and establishing a new generation of inclusive, open, and shared foundational infrastructure for computing power, fully unleashing the potential of global idle hardware resources so that every unit of computing power can be fully utilized, achieving maximum value for computing power resources.

To ensure the efficient implementation of the entire operating system, BTTInferGrid adopts a modular layered architecture design, establishing a three-layer collaborative system of "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 and intelligent agents in diverse scenarios.

• Computing Layer: This is the core hub of computing power for the entire ecosystem, responsible for key duties such as AI model inference calculations, real-time request responses, and task scheduling.

• Settlement Layer: The settlement layer is responsible for the automated operation of the entire economic system, covering the entire process of computing power staking, task settlement, contribution reward distribution, and malicious node penalties. This layer executes on-chain transactions in a trustless manner, ensuring that both parties in the computing power supply and demand achieve fair and transparent value exchange without the need for intermediaries, providing a solid economic trust foundation for the entire network.

The three-layer architecture collaborates efficiently through standardized interfaces: the application layer initiates inference requests, the computing layer schedules computing power resources to complete execution, and the settlement layer automatically completes incentive distribution based on execution results. The three support each other and operate in a closed loop, collectively forming a high-performance, highly trustworthy, and sustainable decentralized AI inference infrastructure.

Relying on the three-layer infrastructure, BTTInferGrid has multiple advantages such as distributed node autonomy, demand-driven permissionless access, and end-to-end trustworthiness and verifiability, establishing an efficient, robust, and open distributed computing power operating environment with no entry barriers.

From a network architecture perspective, BTTInferGrid adopts a globally distributed node deployment strategy, with all nodes jointly owned and operated by the community, with no single data center or operational entity controlling the core of the network. This inherently decentralized design completely avoids the common single point of failure and operational interruption risks of traditional centralized platforms, endowing the network with strong censorship resistance and 24/7 uninterrupted service resilience, providing a highly available operational foundation for various AI inference tasks.

In terms of computing power resource access and scheduling rules, BTTInferGrid implements a permissionless open mechanism: all GPU devices that meet performance standards can freely access the network without centralized institutional review. At the same time, the overall supply of computing power is entirely driven by real business needs, using the actual computing power utilization of nodes and comprehensive service performance as the basis for incentive accounting, complemented by a dynamic supply adjustment mechanism that flexibly allocates resource scale based on real-time network computing power load. This mechanism not only improves the turnover efficiency of computing power resources but also ensures that computing power providers can obtain stable returns that match their contributions over the long term.

At the level of trust mechanisms, BTTInferGrid integrates trust logic throughout the entire business process. The entire network relies on a complete cryptoeconomic system to automatically complete operations such as computing power scheduling, task allocation, and revenue settlement, with every AI inference computation task being fully traceable, and computation results supporting on-chain cross-verification. Through the design of the underlying mechanism, the network eliminates violations such as computing power misreporting and data tampering from the source, ensuring the authenticity and integrity of all computation tasks, allowing demand-side users to use it with confidence and supply-side participants to engage with peace of mind.

In summary, the distributed node architecture endows the computing power network with autonomy and high stability; the demand-driven permissionless access model ensures efficient turnover of computing power and long-term economic sustainability; and the end-to-end verifiable trust system safeguards the ecological security baseline. The deep integration of these three core characteristics makes BTTInferGrid not just a technologically advanced distributed computing power network but also a long-term stable, highly trustworthy, and future-oriented decentralized AI infrastructure.

BTT is Expected to Become the Core Value Token of the Decentralized AI Computing Power Network, with Ecological Application Boundaries Likely to Broaden

As the native value token of the BitTorrent ecosystem, with the official launch of BTTInferGrid and the continuous expansion of the ecosystem, the strategic positioning of BTT may undergo a critical upgrade, with application scenarios potentially extending from traditional distributed transmission and storage tracks to a breakthrough extension across the entire AI computing power infrastructure industry chain, continuously broadening the ecological value boundaries.

In the past, BTT was the circulation carrier of the world's leading decentralized file transfer network BitTorrent; now, relying on the new AI computing power network BTTInferGrid, it is expected to evolve into the core token for scheduling the decentralized AI computing power network, undertaking dual functions of value circulation and ecological governance.

The cryptoeconomic incentive mechanism of BTTInferGrid serves as the underlying engine for network operation, connecting idle GPU computing power off-chain with AI developers' inference needs, achieving automation of 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 mainly relies on the collaborative participation and division of labor among three core roles: miners, users (AI developers), and validators, jointly building a decentralized computing power network that operates autonomously:

• Miners (Computing Power Suppliers): Contribute idle GPU resources, undertake and complete AI inference tasks, and earn corresponding rewards based on actual workload, task completion quality, and dynamic performance ratings.

• AI Developers (Computing Power Demanders): Can access the global distributed computing power pool through a unified standardized API, significantly reducing computing power calling costs.

• Validators (Network Guardians): Audit the computational performance of miner nodes and conduct random challenges to identify node cheating, low-quality computing power, and other abnormal behaviors, earning corresponding 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, driving the continuous evolution and virtuous cycle of the BTTInferGrid ecosystem. The core link that connects the rights and interests of all parties and drives the healthy operation of the ecosystem is the cryptoeconomic incentive system tailored for BTTInferGrid.

This system achieves precise quantification and fair distribution of computing power value through the circulation of tokens, transforming behaviors such as computing power supply, task execution, and result auditing into clear and quantifiable incentive signals: miners who contribute idle GPUs and complete inference tasks with high quality can receive token rewards, validators earn rewards for maintaining network security, and AI developers pay fees based on actual computing power consumption. The interests of the three parties achieve dynamic balance through the circulation of the token economy, thereby constructing a sustainable value closed loop.

Under this framework, BTT is expected to become the unified native incentive and settlement token within the BTTInferGrid ecosystem, covering the core aspects of the entire computing power ecosystem, fully encompassing the payment for the use of AI computing power resources, contribution incentives, and dynamic distribution processes, ultimately building a closed-loop economic system where "computing power contributors receive rewards, computing power users pay conveniently, and ecosystem participants share value."

Specifically, the BTT token can play multiple core roles in the BTTInferGrid network: as a payment medium, AI developers use BTT (or its equivalent tokens) to pay for inference service fees, achieving "on-demand procurement, pay-as-you-go"; as an incentive tool, miners earn token rewards based on their verified actual computational contributions, and validators earn rewards for providing auditing and challenge services, continuously attracting idle resources globally to access the network; as a staking asset, validators must stake tokens to participate in scoring and verification, and computing power nodes must also stake a certain amount of tokens to qualify for task undertaking, with any misconduct triggering a collateral forfeiture mechanism, effectively ensuring the security and fairness of the network from an economic perspective.

Therefore, BTT is expected to not only be a value carrier matching supply and demand for computing power in the future but also the underlying core driving force supporting the efficient, fair, and long-lasting operation of the entire decentralized AI computing economy. On one hand, through token incentives, it continuously attracts more idle GPU resources to access the network and expand computing power supply; on the other hand, the accompanying staking forfeiture mechanism ensures the stability and reliability of inference services. At the same time, all settlement and reward-punishment logic is automatically executed by smart contracts, effectively addressing the common pain points of information opacity and high trust costs in centralized computing power platforms.

With the development and increasing prosperity of the BTTInferGrid ecosystem, BTT is expected to become a universal value anchor connecting distributed computing power and AI application demands, opening a new paradigm for the decentralized AI economy.

BTTInferGrid Restructures Global Computing Power Allocation Mechanism, BitTorrent Opens a New Chapter in the Decentralized AI Track

In the context of the ongoing intensification of global AI computing power supply and demand contradictions and the increasing centralization of computing power monopolies, BTTInferGrid reconstructs the computing power supply model through distributed technology: it efficiently aggregates fragmented idle GPU resources globally, building an open and shared computing power infrastructure that allows AI developers to access flexible computing power with zero barriers, while enabling every unit of idle computing power worldwide to release its inherent value. At the same time, relying on innovative cryptoeconomic incentives and collaborative governance mechanisms, it connects the value circulation closed loop between the supply and demand sides of computing power, forming an ecological cycle of mutual promotion and healthy operation.

For miners (computing power suppliers), BTTInferGrid serves as a "value converter" that can transform idle computing power into continuous revenue. Any idle GPU that meets the basic performance threshold can access the network without permission, contributing computing power to earn rewards.

Unlike the traditional distributed computing power platforms that allocate rewards purely based on "hardware computing power size," BTTInferGrid adopts a multi-dimensional scoring weighted incentive model: the network comprehensively evaluates the actual effective workload, task response latency, service stability, result accuracy, and other core indicators of validator nodes, dynamically calculating and distributing corresponding rewards. This mechanism completely breaks the pattern of "large computing power monopolizing rewards," allowing small and medium miners who provide high-quality, reliable services to also receive excess returns, institutionally ensuring the service quality of the entire network. Additionally, miners who participate early in network construction will enjoy exclusive reward multipliers and other ecological preferential policies, gaining a first-mover advantage.

For AI developers, BTTInferGrid provides developers with open access, verifiable processes, and flexible pay-as-you-go AI inference computing power services, a completely different computing power solution from traditional cloud vendors, effectively addressing the multiple pain points commonly faced in the industry such as "high computing power costs, poor elasticity, and trust issues," significantly lowering the trial-and-error threshold for AI application implementation.

First, BTTInferGrid offers elastic computing power scheduling, dynamically allocating resources based on AI inference loads, allowing developers to avoid pre-purchasing hardware or signing long-term contracts, completely freeing them from the resource lock-in of centralized cloud vendors, truly achieving on-demand use and flexible scaling; secondly, it adopts a decentralized market-based pricing and precise token billing model, eliminating the high premiums of centralized platforms, significantly reducing inference costs and bringing computing power expenditures back to reasonable levels; more importantly, BTTInferGrid has built a decentralized multi-validator auditing network that, through random challenges, cross-verification, and collateral forfeiture mechanisms, eliminates computing power fraud and result tampering at the technical level, ensuring that every inference computation is authentic, traceable, and results verifiable. Multiple advantages complement each other, making BTTInferGrid not only a cost-effective channel for acquiring computing power but also a trusted decentralized AI inference infrastructure for developers.

In terms of product development, BTTInferGrid has established clear, actionable short-term, mid-term, and long-term development plans, steadily advancing the iteration upgrade and ecological expansion of the decentralized AI computing power network:

Short-term goal (2026): Focus on network launch and foundational service implementation, gradually increasing the number of online GPU nodes while completing the launch of core nodes and validating inference services, and adding support for mainstream open-source models such as DeepSeek and Qwen, launching API services for developers and enterprise clients;

Mid-term goal (2027): Focus on expanding the ecological closed loop and capability boundaries, enhancing network performance and ecological richness based on stable operation of inference services, achieving an upgrade from a single inference service to a comprehensive computing power platform (such as model fine-tuning, cross-chain resource access, etc.), and building a complete developer toolchain and ecological support system;

Long-term goal (2028 and beyond): Aim to become the AI-native infrastructure, creating a collaborative network that integrates computing, storage, and smart contracts, providing underlying support for AI agents and automated applications, ultimately becoming the preferred decentralized inference layer for global open-source AI applications, providing elastic, inclusive, and trustworthy computing power support for large-scale, high-concurrency next-generation AI application scenarios.

In terms of ecological construction, BTTInferGrid has now completed native adaptations for several top industry open-source large models, including Alibaba Cloud Tongyi Qianwen Qwen3.6 27B, Qwen2.5 7B Instruct, and Meta Llama 3.1 8B Instruct, covering diverse business scenarios such as general dialogue, code generation, and content creation. Developers do not need to deploy and debug models themselves; they can flexibly call them on demand through standardized API interfaces, further lowering the usage threshold for developers and significantly shortening the development and launch cycle of AI applications.

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

The official launch of BTTInferGrid is not only a milestone strategic layout for BitTorrent in the decentralized AI track but also provides a practical new path for the global AI industry to solve the computing power shortage dilemma. It reconstructs the computing power supply system with decentralized technology, redefining the logic of production, distribution, and value circulation of computing power, breaking the resource monopoly long formed by centralized platforms; at the same time, it promotes the gradual transition of decentralized AI infrastructure from concept validation to large-scale implementation, officially opening the door for distributed computing power to fully empower the next generation of the artificial intelligence industry.