Inference Revolution

Introduction By September 2026, the development of artificial intelligence has collided with an insurmountable physical barrier: a global deficit of electrical power. The era of software optimization is over; progress in AI is no longer measured in model parameters, but in permitted gigawatts of installed capacity and the availability of atomic generation.

This article examines three tectonic shifts defining the infrastructure of the present moment:

The Architectural Limit (Vera Rubin): How Nvidia merged thousands of chips into a single processor exceeding 1 GW of power draw, forcing regulators to tie data center construction permits to the presence of private nuclear reactors (SMRs).
The Escape to Orbit (Project Suncatcher): Why Google is launching TPUs into space to bypass Earth’s energy and cooling constraints, turning orbit into a site for hyperscale data centers.
The Inference Revolution: How the mass business transition from training models to exploiting them (inference) triggered an explosive demand (+507%) for GPU infrastructure and spawned Edge Computing, moving calculations from hyper-CZODs directly to the end-user.
Vera Rubin and the End of Terrestrial Networks

While giants build gigawatt factories and launch satellites, a quiet revolution in consumption is happening on the ground. According to industry reports for the first half of 2026, demand for cloud GPU infrastructure grew by 507% year-over-year. The primary driver was not the research sector, but massive corporate inference — the deployment of ready-made AI solutions into business processes [sources=[4 из предыдущего ответа]].

Document processing and speech analytics.
Corporate knowledge bases and customer support.
Anti-fraud systems and computer vision.

The average check for server rentals rose by 64%, reaching 2.3 million conventional units per configuration. The most sought-after video card remains the NVIDIA A4000 — it provides the ideal balance of price and performance for running already trained models. This proves that the AI economy has shifted from the stage of creation («training») to the stage of operation («inference»). To support this influx of requests, infrastructure must become distributed, bringing computation closer to the user (edge computing), as transmitting data to a central hyper-data center creates unacceptable latency.

However, beneath this «quiet» growth lies a foundation of unprecedented industrial scale: the architecture of Nvidia Vera Rubin.

From Discrete Components to a Unified Supercomputer

Unlike previous generations like Blackwell, the Vera Rubin architecture represents a vertically integrated stack of seven specialized crystals fused into a single system: CPU Vera, GPU Rubin, ConnectX-9 network adapters, BlueField-4 DPUs, and NVLink 6th generation switches.

The key element is the NVL72 rack. Thanks to the NVLink-Spine interconnect with unprecedented throughput of up to 260 TB/s, dozens of accelerators cease to be separate servers and merge into one giant synchronous processor. This approach allowed Nvidia to achieve a qualitative leap: a claimed sevenfold reduction in Total Cost of Ownership (TCO) and a reduction in the number of required GPUs by three-quarters while maintaining cluster power.

The Boundary of the Possible

Radical performance came at the cost of colossal power consumption. A single NVL72 rack generates a heat flow of about 230 kW, and prospective Ultra Kyber systems will require 600 kW per cabinet by 2027. A full-scale new-generation training cluster requires more than 1 GW of continuous energy — a load equivalent to that of a metropolis.

Old current distribution infrastructure is physically incapable of handling such currents without risking regional grid collapse. Regulators responded harshly: a mandatory energy efficiency standard of PUE < 1.1 was introduced. Most importantly, land use rules changed — the construction of hyperscale data centers is now permitted only if they possess their own baseload generation sources. Companies are obliged to build small modular nuclear reactors (SMRs) or geothermal plants nearby. Applications for connection to municipal substations are now automatically rejected. Software Diplomacy for Power Grids To legitimize the consumption of gigawatt-scale capacities, market leaders created the AI Power Management Alliance, including Nvidia, Google, and algorithm developer Emerald AI. The essence of the initiative is the implementation of flexible load regulation technologies (demand response). Neural network training possesses high temporal elasticity. A new software layer will allow data centers to dynamically reduce their appetite during peak loads on city grids, shifting computations to night hours. Thus, hyper-clusters transform from passive consumers into stabilizing buffers for national energy, opening a path to connecting new objects even within an overloaded power market. An Engineering Upheaval Inside the Data Hall The transition to Vera Rubin made traditional data centers obsolete: Total Liquid Cooling: Air systems are deemed ineffective; the industry has fully switched to direct liquid-to-chip contact. High-Voltage Revolution: To minimize losses, 415V AC and above became the new standard for rack power supply. Deployment Speed: Chassis modularity reduced node installation time from two hours to five minutes thanks to wireless internal communication. Continuous Diagnostics: The RAS system monitors the state of each accelerator during the training of trillion-parameter models, eliminating downtime costing millions of dollars. The Vera Rubin platform has fixed a new reality: AI progress is no longer measured in teraflops. It is measured in permitted gigawatts of installed capacity, kilometers of laid transmission lines, and the successes of heavy mechanical engineering. The era of software is over; the era of the heavy energy industry has begun. Exiting the Atmosphere: Project Suncatcher When the expansion of terrestrial infrastructure is limited by ecologists and power engineers, the industry looks for alternative venues. On October 1st, 2026, a SpaceX Falcon 9 rocket will deliver the first experimental satellite of Google's Project Suncatcher into Low Earth Orbit (LEO). Inside a satellite the size of a large household appliance are four custom Tensor Processing Units (TPUs), photovoltaic panels, and experimental thermal management systems. This is not a commercial product, but a harsh engineering test (MVP). Its goal is to determine if neural networks can physically exist in the conditions of open space before trillions are invested there. Physics vs. Gravity The project relies on two fundamental advantages of LEO: Infinite Insolation. The planet's atmosphere absorbs significant solar energy. In orbit, this barrier disappears: panels receive a stable 1361 W/m² unaffected by weather or day/night cycles. The satellite generates about 1 kilowatt — modest power, but with a 100% availability coefficient unattainable for terrestrial solar farms. Natural Heat Dissipation. The chip overheating problem is solved radically. In the absence of air, the only way to shed excess heat is thermal radiation into the void. This eliminates the need to build giant cooling towers and spend megawatts of electricity on liquid cooling pump operations. The Engineering Paradox of the "Hot Start" Space turned out to be a merciless examiner. Developers faced two critical problems impossible to fully simulate in a lab: Radiation Erosion: Streams of charged particles slowly destroy the silicon crystal lattice. Despite TPU passing tests for overloads up to 10g at launch, the real cosmic environment may cause unpredictable memory and logic failures. Thermal Inertia: Radiation works slower than convection. To avoid melting the crystals, engineers were forced to introduce a rigid duty cycle: processors function for only 15 minutes, after which they enter a passive cooling mode via heat pipes. "The first launch is needed to see what works, find points of failure, and apply these findings to future missions," notes Travis Beals, Senior Director of Product for Project Suncatcher. The Math of Star Wars Currently, the economics of space computing look terrifying. Wood Mackenzie estimates: a gigawatt data center in orbit will cost investors $170 billion. A similar object on Earth costs about $50 billion. The main culprit of this disparity is the cost of lifting mass. Delivering one kilogram on a Falcon 9 today costs $2,700–$3,000, with most of the budget going toward creating the radiation-hardened platform itself. However, long-term forecasts change the perspective. Analysts at Mach33 Financial Group are confident that a tipping point will arrive by 2030. If next-generation rockets (Starship) reduce delivery costs to $100–$500 per kg, the balance of power will shift. At the $100/kg mark, orbital data centers will become 25% cheaper than terrestrial ones, as they do not need to pay for land, water supply, or the construction of nuclear stations nearby. Battle of Architectures Outside the Atmosphere Project Suncatcher gives Google a hidden advantage in the semiconductor race. Nvidia's H100 and B200 chips were designed for sterile data center halls. Google's own TPUs are initially adapted for the harsh conditions of radiation and temperature fluctuations. Whoever first creates a truly resilient space processor will capture a trillion-dollar market. An additional bonus is integration with Starlink. Processing data directly in orbit will relieve the narrow communication channels between the grouping's thousands of satellites and Earth. Three Paths for Celestial Infrastructure Massive deployment of such systems carries risks: increased space debris, sky glow for telescopes, and electromagnetic interference. The future of the industry depends on which scenario materializes faster: Breakthrough: Launch costs drop to $100/kg. By mid-century, the first autonomous cloud regions appear in orbit. Traditional providers (AWS, Azure) become tenants leasing capacity from satellite operators. Niche Status: Launch costs hold at $500/kg. Space servers are used only where data is born directly in a vacuum — for example, controlling military drone swarms or aggregating IoT satellite signals. Dead End: Radiation turns out to be more aggressive than protective coatings, and the thermal cycle kills performance. The project is recognized as economically unsustainable after a couple of test launches. The October 1st launch is a hypothesis test on whether intelligence can grow without relying on the planet. If physics favors Google's engineers, IT industry boundaries will expand to the Kármán line, and artificial intelligence will become the first technology to fully master the airless expanse. Conclusion The events of September 2026 draw a line under the era when artificial intelligence developed exclusively in the digital plane. We have witnessed a fundamental shift: AI has ceased to be mere code and has finally established itself as a branch of heavy industry. The Vera Rubin architecture showed that extensive growth on Earth has hit a hard limit of power grids, turning data center construction into a complex state-scale engineering and logistics challenge. The answer to this gravitational challenge is Project Suncatcher — Google's radical bet on offloading computations beyond the atmosphere, where solar energy and vacuum solve power and cooling issues. However, the most significant process for the economy turned out to be the Inference Revolution. While visionaries designed gigawatt factories and orbital platforms, mainstream businesses quietly integrated neural networks into their daily operations. The explosive demand for GPU infrastructure proved that the value of artificial intelligence is now created not at the moment of its birth (training), but in the second of its work (inference) at the end-user's side. This gave rise to the inevitability of Edge Computing and made distributed computational edge nodes the new critical infrastructure. The vector of development is clear. The future of artificial intelligence is no longer written solely in algorithms; it is built from high-voltage cables, heat pipes of space satellites, and microchips capable of surviving radiation exposure. The era of software is over. The era of the physical integration of machine intelligence into planetary and near-Earth infrastructure has begun.