BitTorrent Launches BTTInferGrid to Build a Decentralized AI Inference Computing Power Foundation, Expected to Enable a Comprehensive Rise in BTT Value

On June 17, 2023, the world's leading decentralized file transfer ecosystem BitTorrent announced the launch of its core AI strategic product BTTInferGrid, building a decentralized computing power network for AI inference scenarios.

BTTInferGrid is a major AI product strategically upgraded from BitTorrent’s mature decentralized storage service BTFS. It consolidates years of deep technical experience in core areas such as P2P network protocol design, global distributed node governance, and large-scale resource scheduling, giving the platform inherent advantages in scaling applications and commercial deployment from the outset. The official launch of this product not only marks BitTorrent’s entry into the decentralized AI infrastructure track but also officially opens a new chapter for distributed computing power to empower AI industry development.

Relying on an incentivization system based on cryptoeconomics and a distributed consensus mechanism, BTTInferGrid seamlessly connects global idle GPU computing resources with the diverse inference needs of AI developers. It provides open, verifiable, on-demand efficient inference services for the next generation of AI applications, while enabling idle GPU holders to easily monetize their resources, creating a mutually beneficial ecosystem of supply and demand.

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 greater resource elasticity and resilience; industry-wise, it promotes the transformation of computing power from a scarce monopoly to a freely circulating digital production resource, allowing every GPU holder to participate in value creation and profit sharing, thus fostering a new industry pattern of inclusive, shared, and efficient resource flow.

BitTorrent launches BTTInferGrid to build a decentralized AI inference computing base

“Computing power, algorithms, and data” are the three core elements of AI development, with the strategic value of computing power being elevated to an unprecedented height by 2026. The so-called “computing power shortage” is no longer just a distant warning but has become the primary bottleneck constraining AI progress.

Globally, high-end NVIDIA GPUs continue to see rising rental prices, with hardware supply remaining tight; leading AI companies like OpenAI and Anthropic often face server outages due to insufficient computing resources; even tech giants and top academic institutions are struggling to secure enough power, with some, like SpaceX listed on NASDAQ, admitting in IPO filings that their AI system’s computing needs have far exceeded current market supply, even considering reclaiming previously rented resources from Anthropic. Recently, Microsoft’s Azure cloud platform was reported to urgently seek help from Amazon AWS to rent additional capacity to cope with the surge in code submissions on GitHub during the AI era. Meanwhile, top universities like Stanford and MIT have paused several large model training projects due to insufficient computing power, with some graduate thesis defenses postponed.

Against this backdrop of increasingly severe global supply and demand conflicts for computing power, BTTInferGrid was born. It aims to build a decentralized AI inference network (DePIN) by aggregating scattered idle GPU resources worldwide through decentralization, precisely matching the needs of AI developers, breaking down barriers and monopolies formed by traditional centralized providers, maximizing the utilization of global idle hardware, and establishing an inclusive, open, and shared new infrastructure layer for computing power. This fully unleashes the potential of idle global hardware resources, enabling every unit of computing power to be fully utilized and maximizing its value.

To ensure efficient implementation of the entire system, BTTInferGrid adopts a modular layered architecture, forming a “Application Layer — Computing Layer — Settlement Layer” three-tier collaborative system:

Application Layer: As the service entry point for developers, it provides a user-friendly deployment environment supporting rapid deployment of various native AI applications, such as chatbots and intelligent agents, across diverse scenarios.

Computing Layer: The core hub of the ecosystem’s computing power, responsible for AI model inference calculations, real-time request responses, and task scheduling.

Settlement Layer: Manages the automated operation of the entire economic system, including computing power staking, task settlement, contribution rewards distribution, and malicious node penalties. This layer executes on-chain transactions in a trustless manner, ensuring fair and transparent value exchange between supply and demand without intermediaries, providing a solid economic trust foundation for the entire network.

The three layers communicate efficiently through standardized interfaces: the application layer initiates inference requests, the computing layer schedules resources to execute tasks, and the settlement layer automatically completes incentive distribution based on execution results. These components support each other in a closed-loop operation, forming a high-performance, highly trustworthy, and sustainable decentralized AI inference infrastructure.

Based on this three-layer architecture, BTTInferGrid features advantages such as distributed node autonomy, demand-driven permissionless access, and full-chain verifiability, creating an efficient, robust, and open distributed computing environment with no entry barriers.

From a network architecture perspective, BTTInferGrid employs a globally distributed node deployment strategy, with all nodes community-owned and operated in a decentralized manner, avoiding any single data center or operator controlling the core network. This inherently decentralized design effectively mitigates single points of failure and operational risks common in centralized platforms, providing strong resistance to censorship and ensuring 24/7 uninterrupted service, thus offering a highly available foundation for various AI inference tasks.

Regarding resource access and scheduling rules, BTTInferGrid implements an permissionless open mechanism: all GPU devices meeting performance standards can freely join the network without centralized approval. The overall computing power supply is fully driven by real business demand, with incentives based on actual resource utilization and service performance, complemented by dynamic supply adjustment mechanisms that flexibly allocate resources according to real-time network load. This system improves resource turnover efficiency and ensures long-term stable income for resource providers proportional to their contributions.

On the trust mechanism level, BTTInferGrid embeds trust logic throughout the entire process. The network relies on a comprehensive cryptoeconomic system to automatically handle computing power scheduling, task distribution, and reward settlement. Every AI inference task is fully traceable, with results supporting on-chain cross-verification. The underlying design prevents cheating, data tampering, and other violations, ensuring the authenticity and integrity of all computations, allowing users to trust the system and providers to participate with confidence.

In summary, the distributed node architecture grants the network autonomy and high stability; the demand-driven permissionless access ensures efficient resource flow and long-term economic sustainability; and the full-chain verifiable trust system safeguards ecosystem security. The deep integration of these three core features makes BTTInferGrid not just a technically advanced distributed computing network but also a stable, highly trustworthy, and future-oriented decentralized AI infrastructure.

BTT is expected to become the core value token of the decentralized AI computing network, with ecosystem applications potentially expanding comprehensively

As the native value token of the BitTorrent ecosystem, with the official deployment of BTTInferGrid and ongoing ecosystem expansion, BTT’s strategic positioning is poised for a key upgrade. Its application scenarios are expected to extend from traditional distributed transmission and storage to the entire AI computing infrastructure industry chain, continuously broadening the ecosystem’s value boundaries.

Historically, BTT served as the circulation carrier for BitTorrent, the world’s leading decentralized file transfer network; now, with the new AI computing network BTTInferGrid, it is expected to evolve into the core token for scheduling decentralized AI computing power networks, bearing both value transfer and ecosystem governance functions.

The cryptoeconomic incentive mechanism of BTTInferGrid is the underlying engine of the network. It connects off-chain idle GPU resources with AI inference needs, automating task scheduling, result verification, and reward settlement through token incentives, ensuring demand-supply matching and transparent governance.

Within the BTTInferGrid system, the ecosystem’s continuous operation mainly depends on the collaboration and division of labor among three core roles: miners (computing power providers), users (AI developers), and verifiers (network guardians), jointly constructing a self-governing decentralized computing network:

Miner (Power Provider): Contributes idle GPU resources, undertakes and completes AI inference tasks, earning rewards based on workload, quality, and dynamic performance scores.

AI Developer (Demand Side): Accesses the global distributed computing pool via a unified standardized API, significantly reducing inference costs.

Verifier (Network Guardian): Audits and challenges miner nodes’ performance randomly, identifying cheating or low-quality power, and earns rewards by maintaining network security and service quality.

These three participants form a mutually beneficial, constraint-based closed loop through a decentralized consensus mechanism, driving the continuous evolution and healthy cycle of the BTTInferGrid ecosystem. The core link connecting their interests and ensuring ecosystem operation is the tailored cryptoeconomic incentive system.

This system quantifies and fairly distributes computing power value through token circulation, transforming behaviors such as power contribution, task execution, and result auditing into clear, measurable incentive signals: miners receive token rewards for high-quality inference, verifiers earn rewards for maintaining network security, and AI developers pay fees based on actual resource consumption. The interests of all three are dynamically balanced within the token economy, forming a sustainable value loop.

Within this framework, BTT is expected to become the unified native incentive and settlement token across the BTTInferGrid ecosystem, covering the entire AI computing resource lifecycle—payment, contribution rewards, and dynamic allocation—ultimately creating a closed-loop economy of “contributors rewarded, users paid, and ecosystem shared.”

Specifically, BTT tokens can serve multiple core roles within the BTTInferGrid network: as a medium of payment, enabling AI developers to pay for inference services with BTT (or equivalent tokens) on a pay-as-you-go basis; as an incentive tool, rewarding miners for verified contributions; as collateral, requiring verifiers and nodes to stake tokens to participate in scoring and task acceptance, with penalties for misconduct. These mechanisms effectively ensure network security and fairness from an economic standpoint.

Looking ahead, BTT is poised to become not only a value carrier matching supply and demand but also the fundamental driver for the efficient, fair, and sustainable operation of the entire decentralized AI computing economy. Token incentives will continuously attract more idle GPU resources, while staking and penalty mechanisms will ensure stability and reliability. All settlement, reward, and penalty processes are automated via smart contracts, addressing the opacity and high trust costs common in centralized platforms.

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

BTTInferGrid reconstructs the global computing power allocation mechanism, opening a new chapter in decentralized AI

Amid the ongoing intensification of global AI computing power supply and demand conflicts and the increasing monopolization of centralized resources, BTTInferGrid reconstructs the supply model through distributed technology: it efficiently aggregates fragmented idle GPU resources worldwide, creating an open and shared computing infrastructure that allows AI developers to access elastic computing power with zero barriers, while enabling every idle resource to realize its value. Relying on innovative cryptoeconomic incentives and collaborative governance, it connects the supply and demand sides in a closed-loop value flow, forming a healthy ecosystem cycle driven by mutual reinforcement.

For miners (power providers), BTTInferGrid is a “value converter” that transforms idle computing power into continuous income. Any idle GPU meeting basic performance thresholds can join the network permissionlessly and earn rewards.

Unlike traditional distributed computing platforms that simply allocate rewards based on “hardware capacity,” BTTInferGrid adopts a multi-dimensional scoring and weighted incentive model: the network verifies nodes’ actual effective workload, response latency, service stability, and result accuracy, dynamically calculating and distributing rewards accordingly. This mechanism breaks the “monopoly of large power providers,” allowing small and medium miners providing high-quality, reliable services to earn above-average returns, thus ensuring overall service quality. Early participants in network construction will also enjoy exclusive bonus multipliers and other preferential policies, gaining first-mover advantages.

For AI developers, BTTInferGrid offers open, verifiable, and on-demand AI inference computing services—an entirely different solution from traditional cloud providers—effectively addressing common pain points such as high costs, poor elasticity, and trust issues, greatly lowering the barriers to deploying AI applications.

First, it provides elastic scheduling that dynamically allocates resources based on inference load, allowing developers to avoid pre-purchasing hardware or long-term contracts, breaking free from centralized cloud resource lock-in, and enabling truly on-demand, flexible scaling; second, it employs a decentralized market-based pricing and token-based billing model, removing high premiums from centralized platforms and significantly reducing inference costs, bringing expenses back to reasonable levels; more importantly, BTTInferGrid builds a decentralized multi-verifier auditing network, using random challenges, cross-verification, and collateral penalties to technically prevent cheating and result tampering, ensuring each inference task is traceable and results verifiable. These advantages make BTTInferGrid not only a cost-effective inference resource channel but also a trusted decentralized AI inference infrastructure.

In product development, BTTInferGrid has outlined clear short-term, medium-term, and long-term plans to steadily upgrade the decentralized AI computing network and expand the ecosystem:

Short-term (2026): Focus on network launch and basic service deployment, gradually increasing online GPU nodes, completing core node deployment and inference service validation, and supporting mainstream open-source models like DeepSeek and Qwen, launching API services for developers and enterprises.

Mid-term (2027): Emphasize ecosystem closure and capability expansion, improving network performance and ecosystem richness, upgrading from single inference services to comprehensive computing platforms (e.g., model fine-tuning, cross-chain resource access), and building a complete developer toolchain and ecosystem support system.

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

In ecosystem development, BTTInferGrid has already completed native adaptation for several top open-source large models, including Alibaba Cloud’s Tongyi Qwen 3.6 27B, Qwen 2.5 7B Instruct, and Meta’s Llama 3.1 8B Instruct, covering diverse scenarios such as general dialogue, code generation, and content creation. Developers can call models via standardized APIs without deploying or debugging models themselves, greatly lowering the barrier to AI application development and significantly shortening the development and deployment cycle.

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

The official launch of BTTInferGrid is not only a milestone for BitTorrent’s strategic layout in the decentralized AI track but also a practical new path to solve the global AI industry’s computing power shortage. It reconstructs the supply system of computing power with decentralized technology, redefining the production, distribution, and value flow of computing resources, breaking the long-standing resource monopoly of centralized platforms, and promoting the transition of decentralized AI infrastructure from concept validation to large-scale deployment—marking the beginning of a new era where distributed computing power fully empowers the next generation of AI industry.