BitTorrent March AI Calculator: BTTInferGrid Build a decentralised AI reasoning algorithm network

WithAl AgentTHE GLOBAL AI INDUSTRY FORMALLY MOVED FROM A “PASSIVE RESPONSE” TO AN ENTIRELY NEW PHASE OF “SELF-IMPLEMENTATION” IN THE APPLICATION OF COMPLEX SCENARIOS SUCH AS BUSINESS FLOWS, AUTOMATED PRODUCTION AND SELF-IMPLEMENTATION. THE CORE OF INDUSTRY COMPETITION HAS ALSO LONG MOVED AWAY FROM MERE LARGE MODEL PARAMETERS TO A RACE FOR DOWNLAND ENFORCEMENT CAPACITY, AND A STRONG CAPACITY FOR LOGICAL REASONING IS AT THE HEART OF THE TRANSFORMATION。

A paradigm shift in the application landscape has also led to a fundamental shift in upstream computing infrastructure needs:The weight of power consumption continues to shift from modeling to business reasoningTHIS TREND IS IRREVERSIBLE. HOWEVER, THE CURRENT MAINSTREAM CENTRALIZED COMPUTING SYSTEM, IN THE FACE OF HEAVY, HIGH-FREQUENCY, HIGH-SPEED, HIGH-VOLATILITY REASONING REQUESTS, EXPOSED HIGH OPERATING COSTS, WEAK ELASTICITY, AND INADEQUATE SERVICE STABILITY, AND THE AI INDUSTRY AS A WHOLE IS ENCOUNTERING A SUPPLY-SIDE DEVELOPMENT BOTTLENECK。

June 17th, the old card went to centralize transmission ecologyBitTorrentThe launch of strategic-level products -BTTInferGrid. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . THE PLATFORM IS BASED ON A DECENTRALISED DISTRIBUTIONAL STRUCTURE THAT EFFICIENTLY AGGREGATES SCATTERED, INACTIVE GPU COMPUTING RESOURCES ACROSS THE GLOBE, CONNECTS THE RESOURCE SUPPLY SIDE WITH AI DEVELOPERS, AND PROVIDES OPEN AND EASILY ACCESSIBLE AI REASONING SERVICES THAT CAN BE VALIDATED ON A CHAIN-BASED BASIS, WITH FLEXIBLE COSTING。

It's not just about technologyBTTInferGridNot only have the short panels of conventional centralized algorithms been filled in high-mixed, load-volatilization scenarios, but they have also achieved cross-cutting breakthroughs at the power supply end, reshaping the distribution and flow logic of the entire computing ecosystem。

Meanwhile, BTTInferGrid is BitTorrent based on existingBTFSThe strategic-level product from service upgrades is not only a key extension of BitTorrent ' s decentralised resource-mobilization capability, which will be deep tilled for many years, from the storage track to the computing field, but also a key input for its layout to centralize the AI track。

The structure of computational demand shifted from "training" to "criticism": BTTInferGrid re-engineered AI reasoning algorithms in a decentrized way

BTTInferGrid hopes to re-engineer the power supply system using decentrization models to address the high cost of AI reasoning and supply shortages, while reducing the efficiency of large models, thereby providing industry with high performance, resilience, and high value for money computing infrastructure。

If the years 2024 to 2025 were the Al industry's “Million Moot” and the Vanka cluster-led parameters of an arms race, then in 2026, with the size of AI Agent, it was the beginning of an arms raceAI OFFICIALLY ENTERS THE “ERA OF REASONING” OF MASS APPLICATIONSI DON'T KNOW. AI REASONING IS A KEY LINK IN THE LANDING OF MODEL VALUES THAT TRANSLATES “TRAINED MODELS” INTO PRACTICAL APPLICATIONS, COMMERCIAL VALUES AND DAILY SERVICES. IN SHORT, TRAINING IS “TEACHING AI TO LEARN” AND THE REASONING IS “LETTING AI USE” - FOR EXAMPLE, AUTO-DRIVING VEHICLES RECOGNIZE PARKING SIGNS ON ROADS THAT THEY NEVER TRAVEL. THE ABILITY TO REASON DIRECTLY INFLUENCES THE USER EXPERIENCE OF AI PRODUCTS, THE COST OF OPERATING AND THE COMMERCIAL VALUE。

There's a general consensus in the industry70%Calculator resources will be used for the reasoning scene. Oracle had predicted that the size of the market for reasoning would ultimately exceed training. In the same vein, Zheng Laimin, a member of the Chinese Engineering College, pointed out that most of the current computing power is consumed by daily interaction between users and large models. In terms of cost composition, large model reasoning costs only 3% manpower and 2% dataIt's up to 95%; head application calculation costs are considerable, ChatGPT costs approximately $700,000 per day and DeepSeek V3 amounts to $87,000。

WHEN AI ' S COMPUTING NEEDS SPREAD FROM CENTRALIZED TRAINING OF A FEW TECHNOLOGY GIANTS TO THE BUSINESS REASONING SCENE OF MILLIONS OF DEVELOPERS IN ALL WALKS OF LIFE, THE STANDARDS OF THE BOTTOM INFRASTRUCTURE CHANGED. IN THE TRAINING AGE, DEVELOPERS FOCUSED MAINLY ON THE SCALE AND EFFICIENCY OF COMPUTING; IN THE AGE OF REASONING, AI SERVICES WERE DIRECTED DIRECTLY TOWARDS BIG END-USERS, WITH AN AVERAGE OF HUNDREDS OF MILLIONS OF INTERACTIVE AND MASSIVE COMPUTING TIMES PER DAY, AND DEVELOPERS ' ATTENTION SHIFTED TO THE COST OF EACH CALL, SPEED OF RESPONSE AND STABILITY OF SERVICES. TODAY, POWER SUPPLY, CALL COSTS, SERVICE AVAILABILITY HAVE BECOME A CENTRAL BASIS FOR ASSESSING AI INFRASTRUCTURE AND ARE KEY TO DETERMINING WHETHER AI APPLICATIONS CAN BE SUCCESSFULLY LANDED。

HOWEVER, IN THE FACE OF THE NEED FOR THE REASONING OF INDEX ESCALATION, THE SHORT BOARD OF THE MAINSTREAM CENTRALIZED COMPUTING SYSTEM HAS BECOME INCREASINGLY PROMINENT: THE CONSTANT RISE IN THE RENT OF THE GPU, THE FREQUENT LOSS OF PLATFORM SERVICES, AND THE FACT THAT MANY AI APPLICATIONS HAVE BEEN SHUT DOWN BECAUSE OF THE COST OF COMPUTING. THESE PROBLEMS ARE CONCENTRATED IN THREE AREAS:

• First, there is insufficient flexibility in computing to cope with changes in the peaks of traffic and to be caught in an imbalance between costs and stability:WHILE HEAD AI AND CLOUD MANUFACTURERS CONTINUE TO INCREASE THEIR INPUT TO COMPUTING FACILITIES, THE REASONING DEMAND IS GROWING RAPIDLY AND IS CHARACTERIZED BY CLEAR PEAKS — REQUESTS CAN INCREASE DRAMATICALLY BY SEVERAL DOZEN TIMES DURING DAYTIME WORK OR MARKETING PEAKS; LATE NIGHTS FALL OFF THE CLIFF. THE CENTRALIZATION ROOMS LACK THE FLEXIBILITY TO ADAPT TO SUCH DYNAMIC CHANGES: WHEN CONFIGURED AT PEAKS, DEPRECIATION COSTS ARE HIGH AT LOW PEAKS; WHEN CONFIGURED AT AVERAGES, SERVICES ARE INTERRUPTED AT PEAKS, FALLING INTO A DILEMMA BETWEEN “HIGH COSTS” AND “LOW STABILITY”. AT THE SAME TIME, CENTRALIZATION REQUIRES MULTIPLE LAYERS OF COST, SUCH AS BUILDING ROOMS, ELECTRICITY, TRANSPORTATION, COMMERCIAL PROFITS, ETC., WHICH WILL ULTIMATELY BE COSTLY AND WILL SIGNIFICANTLY REDUCE THE RISK SPACE FOR SMALL AND MEDIUM-SIZED INNOVATIVE TEAMS, AND THERE IS AN URGENT NEED FOR A NEW MARKET THAT COMBINES COST ADVANTAGES WITH FLEXIBLE DISPATCH CAPABILITIES。

• SECOND, THE CONTINUED RISE IN THE RENTAL PRICES OF GPUS, WITH HIGH COSTS PREVENTING SMES AND DEVELOPERS FROM BECOMING INNOVATIVE:While large open source models (e.g. Qwen, DeepSeek, etc.) have lowered the threshold for entry into the AI domain, their deployment and operation continue to rely on stable, inexpensive and easily accessible reasoning. The reality, however, is that the GPU rental costs are rising, using the dominant H100 card, where the price of a single card is rising from US$ 1.70 in October 2025 to US$ 2.35 in March 2026, a six-monthly increase of nearly 40 per cent. High costs have deterred many individual developers with high-quality programmes and SMEs from being “modeled, incompetent” and severely inhibiting AI's innovation dynamism and scale-up。

• THIRDLY, LARGE AMOUNTS OF IDLE GPU RESOURCES WORLDWIDE ARE NOT BEING USED EFFECTIVELY AND SUPPLY AND DEMAND ARE SEVERELY MISMATCHED:IN CONTRAST TO THE MARKET'S “CALCULATIVE POWER”, LARGE IDLE HIGH-PERFORMANCE GPU COMPUTING RESOURCES ARE DEPOSITED GLOBALLY, SCATTERED AMONG PERSONAL EQUIPMENT, UNIVERSITY LABORATORIES, SMALL ROOMS AND FACILITIES LEFT OVER FROM THE ENCRYPTED CURRENCY TRANSITION. THE LACK OF STANDARDIZED ACCESS AND EFFICIENT DISPATCH ENGINES HAS PREVENTED THESE CALCULATIONS FROM ENTERING THE MAINSTREAM REASONING MARKET, CREATING A CONFLICT BETWEEN DEMAND-SIDE “HARD-LOADING” AND SUPPLY-SIDE “SLEEPING-IN” AND A HUGE INCREASE IN RESOURCE UTILIZATION, WITH SUPPLY-DEMAND MISMATCHES TO BE URGENTLY RESOLVED。

In conclusion, the AI reasoning market is facing a triple structural dilemma: On the one hand, centralised supply cannot be balanced between cost and elasticity, on the other hand, computing rents continue to skyrocket AI innovation, and on the other hand, large idle GPU resources remain unactivating. In the face of this set of industry challenges, BTTInferGrid, relying on decentrized technologies, has brought new solutions to the problem of the mismatch between demand and supply of computing power。

BTTInferGridThe aim is to effectively connect globally dispersed inactive GPU resources to big AI developers in a decentrized manner, fundamentally breaking centralization monopolies and bottlenecks. On the one hand, the platform integrates fragmented and inactive GPU computing power and constructs open and shared computing infrastructure; on the other hand, it connects the supply side with the demand side and removes barriers to access and price-fixing black boxes from traditional centralization models. At the same time, relying on DePIN’s incentive and synergy mechanisms, BTTInferGrid is able to continue to export high-value reasoning, remove the core pains of high-cost calculations and supply shortages from the root causes, and truly unleash the reasoning effectiveness and commercial value of large models。

BTTInferGrid: Build a decentrified computing network for AI reasoning scenarios, redefinition of the three main advantages of the algorithm

BTTInferGrid is clearly positioned and focused on the construction of a decentralised computing network for the AI reasoning scene, linking the global idle GPU calculator supply to AI reasoned market demand, and providing open access, results legibility, and cost-based, global AI computing services。

In particular, BTTInferGrid, relying on the DePIN bottom network mechanism, is precise enough to match the AI reasoning demand for energy supply and explosive growth to achieve a two-way value enabling both supply and demand:

• The power supply side:Efficiently synthesizing global debrisation idle GPU resources with open and shared algorithms. With DePIN’s incentive and smart dispatch mechanisms, it has created low-threshold, sustainable realization revenue pathways for the holders of computing power, making the world’s idle “sleeping GPU” truly a “floating asset” and safeguarding the stability and elasticity of computing power, creating a high value-for-money, high-extension, safe and reliable global reasoning service。

• Calculator demand side:A global AI developer with easy access, a chain-based validation of results, and cost-based global reasoning services. Compared to high premium pricing by central cloud manufacturers, BTTInferGrid has the greatest cost advantage and elasticity capacity to help small and medium-sized creative teams, independent developers to reduce the cost of doing business trials, efficiently complete product validation and business overlaps, while at the same time providing ecological energy upstream to reverse empowerment。

As a result, BTTInferGrid has effectively addressed the pressing needs of AI developers for low-cost, high-flexible computing during the “applying puzzle” phase, and has opened sustainable value realization pathways for large idle hardware resources worldwide。

More importantly, the BTTInferGrid platform will successfully build a positive growth wheel for self-sufficiency: idle GPU nodes continue to expand, reasoning costs continue to decrease, attracting more developers; and market demand continues to rise, further stimulating global computing suppliers to join the ecology. The BTTInferGrid re-engineered the supply of energy in a decentrified mode to transform scarce, high-priced dedicated AI computing into an inclusive, needs-based AI public base building。

In terms of product performance advantages, most of the current market is de-centreized GPU platforms, with widespread problems such as the high threshold of computing access, the lack of credibility of services and the difficulty of long-term operation of economic models. BTTInferGrid, in turn, optimizes the bottom structure to achieve a comprehensive breakthrough in the three dimensions of aggregation, service validation and sustainability of the economic system, creating unique core competitiveness, with the following advantages:

1. AN OPEN-ACCESS CALCULATOR NETWORK THAT QUICKLY BRINGS TOGETHER IDLE GPU RESOURCES WORLDWIDE:Traditional cloud computing has a high threshold of access (e.g. compliance room, fixed public network IP, expensive switchboard, etc.), and BTTInferGrid has built up a truly open-access computing network, with seamless access to any entity or individual with a non-existent computing resource such as GPU, provided that it meets the basic performance parameters (e.g., visible storage capacity, algorithms) and network stability requirements. This design significantly lowers the threshold of participation on the side of the supply of computing resources, allowing global idle GPU computing to be networked and matrixed at very high speeds。

2. Qualifiable quality of service and nodal behaviour to solve the centralization of trust:The biggest pain in going to centre is credibility — how to prevent miners from impersonating high-performance cards with low-end graphic cards? How can we ensure that the reasoning is credible? BTTInferGrid has effectively enhanced the credibility of reasoning services by creating cross-validated closed loops through mission movement (intelligence distribution), challenge validation (circular spot-checking), consensus rating (dynamic credit rating) and chain coordination (intelligence contract awards and sanctions)。

3. Demand-driven economic models for sustainable ecology:The early DePIN project was often caught in the death spiral of “high tokens to attract nodes blindly to mine, but in the absence of real demand, it moved towards currency inflation, price collapses, nodes exits”. BTTInferGrid has established, from its inception, the creation of an economic ecology driven by real demand — based on genuine reasoning and nodal representation as a core incentive. Only when AI developers actually pay for the call model can the computing provider obtain a core share of proceeds plus credibility. This design will provide a powerful impetus for a healthy growth in the scale of supply and market demand, ensuring a healthy and sustainable web-based ecology over the long term。

In conclusion, from breaking the traditional threshold of access, allowing open and seamless access to GPUs that meet global standards of performance, to a full process of mission scheduling, challenge validation, consensus scoring and chaining that can validate a trust line of defence, to a complete departure from speculative bubbles, to a demand-driven economic model that sets the incentive anchor for real AI reasoning — BTTInferGrid — is redefining mechanisms for allocating power resources from the three dimensions of resource pooling, service credibility and value allocation。

BTTInferGrid will be phased in to create a new computing ecology driven by real demand

BTTInferGrid is not a simple "calculation aggregation" but a sophisticated, multifunctional, off-centre computing network that combines AI reasoned task movement and execution, intelligent matching and linking of power supply and demand, chain resource coordination and liquidation。

In the centralized computing ecology of BTTInferGrid, three core roles were formed around the “supply, use and validation” of arithmetic:

• Energy supply side (miners):PROVIDE IDLE GPU RESOURCES, TAKE OVER AND PERFORM AI REASONING TASKS, AND THE SYSTEM AUTOMATICALLY DISTRIBUTES THE CORRESPONDING INCENTIVES BASED ON VALIDATED ACTUAL WORKLOAD, QUALITY AND DYNAMIC PERFORMANCE。

• CALCULATOR DEMAND SIDE (AI DEVELOPER):BTTInferGrid provides a standardized API service interface to support developers ' access to global distributed GPU resources。

• Webkeeper (certifier):Participation in decentrized validation and rating systems, auditing and random challenges in the calculation of the miners ' nodes, identifying anomalies and maintaining the quality of network services. At the same time, the certifying officers are rewarded for maintaining the integrity of the network and jointly safeguard its fairness and credibility。

In summary, for AI developers, BTTInferGrid offers more cost-effective, scalable and secure AI reasoning services that effectively mitigate product disruptions and client losses due to inadequate computing. For GPU providers, the creation of a sustainable revenue path for GPU resource providers at the global edge and idle hardware resources allows each calculus to be of value in the age of reasoning。

On the ground of specific products, unlike the heavy asset model of the traditional central cloud manufacturer, who “stacks hardware first, waits for demand”, DePIN faced the natural two-way coordination challenge in the early stages of construction — oversupply leading to idleness and the collapse of the currency economy, and undersupply could undermine the development experience and system efficiency. To this end, BTTInferGrid has developed a clear, robust and demand-driven phased start-up strategy, moving away from random growth and prioritizing resource utilization, economic sustainability and the steady expansion of the technological architecture。

• Short-term target (2026): cyber cold start, COMPLETE THE VALIDATION OF THE BOTTOM CORE NODE ACCESS AND DISTRIBUTED REASONING SERVICES AND GRADUALLY EXPAND THE GPU NODE SIZE。

• Medium-term goal (2027): ecological diversity, IMPROVE THE STABILITY AND PRIVACY OF NETWORK SERVICES, WHILE ALLOWING FOR MORE AI MODEL FORMATS AND REASONING FRAMEWORKS, AND GRADUALLY FINE-TUNE THE APPLICATION SCENARIOS TO MODELS。

• LONG-TERM OBJECTIVE (2028 AND BEYOND): TO BECOME AI BASE INFRASTRUCTURE, build the first-chosen calculator layer for AI Agent and automation applications, provide flexible calculator support for large-scale AI applications, and eventually enable computing, distributed storage and chain smart contracts to work in a unified architecture。

In landing implementation, BTTInferGrid also follows a phased evolution strategy. In the early days, the network was dominated by professional graphic cards, access to the power supply side (miners) was subject to review and demand side users could call for reasoning services through the platform. In the future, it will evolve into a fully open super-calculative grid that supports a variety of GPU types, such as the consumer, professional, data centre levels, and provides a hierarchy of access and pricing according to performance; miners open access with the introduction of pledge mechanisms to safeguard the quality of services; and demand side open to a unified API interface that is compatible with multiple AI model formats and reasoning frameworks and provides flexible deployment options。

Currently, BTTInferGrid has successfully accessed multiple mainstream AI open source models, including Aliyun Qwen seriesQwen3.6 27BandQwen2.5 7B InsuranceAnd Meta'sLlama 3.1 8B ImpactI DON'T KNOW. AI DEVELOPERS CAN ACHIEVE ON-DEMAND FLEXIBILITY BASED ON ACTUAL BUSINESS SCENARIOS. IN THE FUTURE, THE PLATFORM WILL CONTINUOUSLY EXPAND MODEL ECOLOGY TO PROVIDE MORE FRONT-LINE MODEL SUPPORT TO DEVELOPERS。

More importantly, BTTInferGrid has the long-term accumulation of BitTorrent and BTFS as a solid back-up and a natural development advantage. BitTorrent with the flag BTFS has been working deep in the field of centralized storage for many years, in which BitTorrent hasOver 100 million active users and 2 billion installedThe feasibility of the DePIN model has been successfully validated and mature capacity for resource access, token incentives, chain settlements, community operations, etc. As a strategic product of BitTorrent layout AI, BTTInferGrid, based on the upgrading of existing BTFS services, can seamlessly migrate these mature experiences to the AI reasoning field and can contribute rapidly to ecological growth。

BTTInferGrid, based on the centralization technology, deciphered the industrial dilemma of “calculative idleness” alongside “calculations shortage”. Its concept of open access, decentrized collaboration, verifiable contributions and community-building is not only a powerful breakthrough in the traditional centralization of computing power, but also a new vision of global centralization based on clear product positioning and solid technology. Every idle calculator here will be activated, and every developer will be able to reach an intelligent future at an inclusive cost。