BitTorrent进军AI算力：BTTInferGrid构建去中心化AI推理算力网络
As AI agents are deployed in various complex scenarios such as enterprise workflows, automated production, and autonomous execution, the global AI industry has officially entered a new "autonomous execution" phase beyond "passive response." The core of industry competition is no longer merely about comparing large model parameters but has shifted to the contest of execution capabilities. **Strong logical reasoning ability is the fundamental pillar supporting this transformation.**
This paradigm shift in application scenarios has also driven a fundamental change in upstream computing infrastructure demand: the focus of computing power consumption is irreversibly tilting from model training to business inference. However, the current mainstream centralized computing systems, when faced with massive, high-frequency inference requests with sharp peak-to-valley fluctuations, expose problems such as high operational costs, weak elastic scaling, and insufficient service stability. The entire AI industry is facing a development bottleneck in computing power supply.
On June 17, the established decentralized transmission ecosystem BitTorrent launched a strategic product——BTTInferGrid, targeting the AI inference track and building a decentralized computing network. The platform leverages a decentralized distributed architecture to efficiently aggregate scattered idle GPU computing resources worldwide. It bridges the connection barriers between resource suppliers and AI developers, providing open, easily accessible AI inference computing services with blockchain-verifiable computation results and flexible pay-per-use billing.
Leveraging the advantages of decentralized technology, BTTInferGrid not only fills the gaps of traditional centralized computing in high-concurrency and load-fluctuation scenarios but also achieves a leapfrog breakthrough on the computing supply side, restructuring the resource allocation and flow logic of the entire computing ecosystem.
At the same time, BTTInferGrid is a strategic product upgraded by BitTorrent based on its existing BTFS service. This is not only BitTorrent's key extension of its years-deep decentralized resource scheduling capability from the storage track to the computing field but also a crucial move in its layout within the decentralized AI track.
Computing Demand Structure Shifts from "Training" to "Inference": BTTInferGrid Reconstructs AI Inference Computing Supply in a Decentralized Way
BTTInferGrid aims to restructure the computing supply system through a decentralized model, solving problems like high AI inference computing costs and supply shortages. It seeks to reduce costs and improve efficiency while enhancing the inference efficiency of large models, thereby providing the industry with high-performance, resilient, and cost-effective computing infrastructure.
If 2024 to 2025 was the era of the "war of a thousand models" and a parameter arms race dominated by ten-thousand-card clusters, then 2026, with the large-scale deployment of AI agents, marks the official entry of AI into the "Inference Era" of massive application explosion. AI inference is the key link for realizing model value; it transforms "trained models" into practical applications, commercial value, and everyday services. Simply put, training is "teaching AI to learn," while inference is "letting AI use its knowledge"—for example, an autonomous driving car recognizing a stop sign on a road it has never driven on is a typical inference behavior. Inference capability directly influences the user experience, operational costs, and commercial value of AI products.
There is a broad consensus within the industry that over 70% of future computing resources will be used for inference scenarios. Oracle has predicted that the market size for inference computing will eventually surpass that of training computing. Zheng Weimin, an academician of the Chinese Academy of Engineering, also pointed out that the vast majority of current computing power is consumed in the daily interaction between users and large models. Looking at cost structure, labor accounts for only 3% of large model inference expenses, data accounts for 2%, and computing power accounts for a staggering 95%; the computing costs for leading applications are substantial, with ChatGPT's daily inference cost around $700,000 and DeepSeek V3 at $87,000.
When AI computing demand shifts from concentrated training by a few tech giants to commercial inference scenarios for millions of developers across various industries, the criteria for evaluating underlying infrastructure also change. In the training era, developers primarily focused on the scale and efficiency of centralized computing power. Entering the inference era, where AI services directly face hundreds of millions of end-users and hundreds of billions of daily interactions generate massive computing consumption, developers' focus shifts to the cost per call, response speed, and service stability. Today, computing supply, call costs, and service availability have become the core criteria for evaluating AI infrastructure and key factors determining whether AI applications can be successfully deployed.
However, facing the exponentially growing inference demand, the shortcomings of the mainstream centralized computing system are becoming increasingly prominent: rising GPU rental prices, frequent platform service outages, and many AI applications being forced to shut down due to computing costs. These issues are manifested in the following three aspects:
First, insufficient elasticity in computing scheduling, inability to cope with traffic peaks and valleys, leading to an imbalance between cost and stability: Although leading AI companies and cloud providers continue to increase investment in computing facilities, inference demand grows rapidly and exhibits obvious peak-valley characteristics—requests can surge dozens of times during daytime office or marketing peaks and drop sharply late at night. Centralized data centers lack elastic scheduling capabilities to adapt to this dynamic change: configuring for peak loads results in high depreciation costs during off-peak hours; configuring for average loads leads to service interruptions during peak times, creating a dilemma between "high cost" and "low stability." Furthermore, centralized computing carries additional layers of cost, including data center construction, electricity, operations, and profit margins, ultimately leading to high computing costs that severely limit the trial-and-error space for small and medium-sized innovative teams. The market urgently needs a new solution that combines cost advantage with elastic scheduling capabilities.
Second, the rising cost of GPU leasing hinders innovation and deployment for SMEs and developers: While open-source large models (such as Qwen, DeepSeek) have lowered the entry barrier to the AI field, model deployment and operation still rely on stable, cheap, and easily accessible inference computing power. However, the reality is that GPU leasing costs continue to rise. Taking the mainstream H100 graphics card as an example, its hourly rental price increased from $1.70 in October 2025 to $2.35 in March 2026, an increase of nearly 40% in six months. The high cost discourages many individual developers and SMEs with excellent solutions, trapping them in a "have models, no computing power" dilemma, which severely inhibits innovation vitality and large-scale development in the AI industry.
Third, a vast amount of global idle GPU resources are not effectively utilized, resulting in a severe mismatch between supply and demand: In stark contrast to the market's "computing power shortage," there is a huge amount of idle high-performance GPU computing resources scattered globally across personal devices, university labs, small data centers, and facilities left over from cryptocurrency transitions. Due to the lack of standardized access channels and efficient scheduling engines, these resources cannot enter the mainstream inference market, creating a contradictory situation where "cards are hard to find" on the demand side while "computing resources lie dormant" on the supply side. There is immense potential to improve resource utilization, and the contradiction of supply-demand mismatch urgently needs resolution.
In summary, the current AI inference computing market faces three structural dilemmas: centralized supply cannot balance cost and elasticity; soaring computing rents suppress AI innovation; and a vast amount of idle GPU resources remain dormant. Facing these industry-wide challenges, BTTInferGrid leverages decentralized technology to offer a novel solution to the supply-demand mismatch.
BTTInferGrid aims to efficiently connect globally dispersed idle GPU resources with a massive number of AI developers through a decentralized approach, fundamentally breaking the monopoly and bottleneck of centralized computing. On one hand, the platform integrates scattered idle GPU computing power to build an open and shared computing infrastructure. On the other hand, it opens the connection channel between the supply and demand sides, eliminating the entry barriers and pricing opacity of traditional centralized models. At the same time, leveraging the incentives and coordination mechanisms of DePIN, BTTInferGrid can continuously deliver cost-effective inference computing power, addressing the core pain points of high costs and supply shortages from their roots, and truly unleashing the inference efficacy and commercial value of large models.
BTTInferGrid: Building a Decentralized Computing Network for AI Inference, Three Major Advantages Redefine Computing Distribution Mechanisms
BTTInferGrid has a clear and specific positioning: focusing on building a decentralized computing network for AI inference scenarios, connecting global idle GPU supply with AI inference market demand, and providing a global AI computing service system that is open for access, offers verifiable results, and uses pay-per-use billing.
Specifically, BTTInferGrid relies on the underlying DePIN network mechanism to precisely match computing supply with the explosively growing AI inference demand, achieving value empowerment for both ends of the supply chain:
· On the Computing Supply Side, it efficiently aggregates fragmented global idle GPU resources to build an open and shared computing foundation. By leveraging DePIN's incentive and intelligent scheduling mechanisms, it provides a low-barrier, sustainable monetization channel for resource holders, turning "dormant GPUs" into "liquid assets." Simultaneously, it ensures computing stability and elastic scaling, creating a high-cost-performance, highly scalable, secure, and reliable global inference service capability.
· On the Computing Demand Side, it offers global AI developers a convenient access, chain-verifiable results, and pay-per-use global inference service. Compared to the high premiums of centralized cloud providers, BTTInferGrid offers extreme cost advantages and elastic scaling capabilities, helping small and medium-sized tech teams and independent developers reduce trial-and-error costs, efficiently complete product validation and iteration, while conversely empowering the upstream computing supply ecosystem.
Thus, BTTInferGrid not only effectively addresses AI developers' urgent need for low-cost, high-elasticity computing during the "application competition" phase but also opens a sustainable value realization channel for massive global idle hardware resources.
More importantly, the BTTInferGrid platform will successfully build a self-sustaining positive growth flywheel: as idle GPU nodes continuously expand, inference computing costs keep decreasing, attracting more developers; increasing market demand further incentivizes global computing providers to join the ecosystem. By restructuring computing supply through a decentralized model, BTTInferGrid transforms scarce, expensive dedicated AI computing power into universal, on-demand AI public underlying infrastructure.
Regarding product performance advantages, many current decentralized GPU platforms commonly face issues like high barriers to computing access, insufficient service trustworthiness, and unsustainable economic models. BTTInferGrid, starting from the underlying architecture, achieves comprehensive breakthroughs in three dimensions: computing aggregation, service verification, and economic sustainability, forming unique core competitiveness:
1. Open Access Computing Supply Network, Rapidly Aggregating Global Idle GPU Resources: Traditional cloud computing has high entry barriers (requiring compliant data centers, fixed public IPs, expensive switches, etc.). BTTInferGrid builds a truly open access computing supply network where any entity or individual with idle GPU resources can seamlessly connect, provided they meet basic performance parameters (e.g., VRAM capacity, benchmark performance) and network stability requirements. This design significantly lowers the participation threshold on the supply side, enabling global idle GPU resources to be aggregated into a networked, matrixed resource pool at high speed.
2. Verifiable Service Quality and Node Behavior, Solving Decentralized Trust Issues: The biggest pain point for decentralized computing is trustworthiness—how to prevent miners from using low-end GPUs pretending to be high-performance ones? How to ensure inference results are authentic and reliable? BTTInferGrid builds a cross-verifiable closed-loop system through task scheduling (intelligent distribution), challenge verification (cryptographic spot checks), consensus scoring (dynamic reputation scores), and on-chain coordination (smart contract rewards/penalties), effectively enhancing the trustworthiness of inference services.
3. Demand-Driven Economic Model, Building a Sustainable Ecosystem: Early DePIN projects often fell into a "death spiral" of high token issuance attracting nodes for blind mining, followed by token inflation, price crashes, and node departure due to a lack of real demand. From its inception, BTTInferGrid established a goal to build an economy driven by real demand—using actual inference calls and node performance as core incentive criteria. Only when an AI developer truly pays for a model call can the computing provider obtain core revenue sharing and reputation boosts. This design will strongly promote a healthy, balanced growth between supply scale and market demand, ensuring the long-term, healthy, and sustainable development of the network ecosystem.
In summary, from breaking traditional entry barriers with an open supply grid allowing any globally compliant idle GPU to connect seamlessly, to a full-process verifiable trust defense line built on four closed loops of task scheduling, challenge verification, consensus scoring, and on-chain rewards/penalties, to completely abandoning speculative bubbles and anchoring incentives on a demand-driven economic model based on real AI inference calls—BTTInferGrid is redefining the allocation mechanism of computing resources from the three dimensions of resource aggregation, service trustworthiness, and value distribution.
BTTInferGrid Will Build a New Computing Ecosystem Driven by Real Demand in Phases
BTTInferGrid is not a simple "computing aggregator" but a sophisticated decentralized computing network integrating multiple functions, including AI inference task scheduling and execution, intelligent matching of computing supply and demand, and on-chain resource coordination and settlement.
Within the BTTInferGrid decentralized computing ecosystem, all participants form three core roles around the "supply, usage, and verification" of computing power:
· Computing Suppliers (Miners): Provide idle GPU resources, accept and execute AI inference tasks. The system automatically distributes corresponding rewards based on verified actual work volume, task completion quality, and dynamic performance scores.
· Computing Consumers (AI Developers): BTTInferGrid provides a standard unified API service interface, allowing developers to access globally distributed GPU resources.
· Network Guardians (Verifiers): Participate in the decentralized verification and scoring system, auditing and randomly challenging the computational performance of miner nodes, identifying anomalous behavior, and maintaining network service quality. At the same time, verifiers receive rewards for maintaining network integrity, collectively ensuring the fairness and trustworthiness of the network.
In summary, for AI developers, BTTInferGrid offers a more cost-effective, highly scalable, secure, and trustworthy AI inference service, effectively mitigating product interruptions and customer churn caused by insufficient computing power. For GPU providers, it activates global edge and idle hardware resources, establishing a sustainable revenue channel for GPU resource owners, ensuring every bit of computing power realizes its value in the inference era.
In terms of specific product deployment, unlike traditional centralized cloud providers' asset-heavy model of "stacking hardware first, waiting for demand," DePIN inherently faces a two-way coordination challenge in its early stages: excess supply leads to idle nodes and token economy collapse, while insufficient supply harms developer experience and system efficiency. To address this, BTTInferGrid has formulated a clear, robust, demand-oriented phased launch strategy, abandoning disorderly extensive growth and prioritizing resource utilization, economic sustainability, and the steady expansion of the technical architecture.
· Short-Term Goal (2026): Network cold start, complete core underlying node access and distributed inference service verification, gradually expanding the scale of GPU nodes.
· Medium-Term Goal (2027): Ecosystem diversification, improving network service stability and privacy security, while simultaneously being compatible with more AI model formats and inference frameworks, gradually extending to application scenarios like model fine-tuning.
· Long-Term Goal (2028 and beyond): Become an AI-native underlying infrastructure, building the preferred computing layer for AI agents and automated applications, providing elastic computing support for large-scale AI applications, and ultimately achieving coordinated operation of computing, distributed storage, and on-chain smart contracts within a unified architecture.
In terms of execution, BTTInferGrid also adopts a phased evolution strategy. In the initial launch phase, the network will primarily use professional-grade GPUs; access on the computing supply side (miners) will require review, while users on the demand side can call inference services through the platform. In the future, it will evolve into a fully open super computing grid: supporting various GPU types (consumer-grade, professional-grade, data center-grade), with access and pricing tiered by performance; open access for miners, along with the introduction of a staking mechanism to ensure service quality; and open unified API interfaces on the demand side, compatible with multiple AI model formats and inference frameworks, offering flexible deployment options.
Currently, BTTInferGrid has successfully integrated several mainstream open-source AI large models, including Alibaba Cloud Qwen series Qwen3.6 27B and Qwen2.5 7B Instruct, as well as Meta's Llama 3.1 8B Instruct. AI developers can flexibly call upon them on-demand based on actual business scenarios. In the future, the platform will continuously expand its model ecosystem, providing developers with support for more cutting-edge models.
More importantly, BTTInferGrid has the solid backing of BitTorrent and BTFS's long-term accumulation, providing inherent development advantages. BitTorrent and its BTFS have been deeply involved in the decentralized storage field for years. BitTorrent itself boasts over 100 million active users and 2 billion installations, having successfully validated the DePIN model and accumulated mature capabilities in resource access, token incentives, on-chain settlement, and community operations. As BitTorrent's strategic product for the AI track, upgraded from the existing BTFS service, BTTInferGrid can seamlessly transfer these mature experiences to the AI
