In 2026, the fundamental architecture and competitive dynamics of data centers will experience radical transformation. This revolution is being propelled by two pivotal forces: the exponential surge in AI computing demands is fundamentally reconfiguring data centers' technical foundations, while shifting global supply chains and sovereignty mandates are restructuring the entire computing ecosystem at an industrial scale.

First, propelled by surging AI computing demands, heterogeneous computing will evolve from mere hardware stacking to system-level optimization. Meanwhile, to address the exponential growth in AI workloads, the industry will not only face the power challenges of gigawatt-scale data centers but also witness liquid cooling transition from an optional solution to a mandatory requirement.Additionally, amid shifting global supply chains and the push for technological self-sufficiency, domestic computing solutions are rapidly advancing—moving from basic usability to high-performance, user-friendly system-level breakthroughs.

So, what key technological changes can we expect in data centers by 2026? This article will explore these critical developments in detail.

Heterogeneous Computing: Evolving from Hardware Stacking to Systemic Architecture

Facing the relentless explosion of AI computing demands, the traditional "CPU+GPU" stacking model has hit its performance and energy efficiency ceiling. By 2026, heterogeneous computing will move beyond the crude era of hardware accumulation and enter a phase of systemic evolution. The key lies in achieving leapfrog improvements in computing efficiency through coordinated innovation at the chip, architecture, and system levels.

At the chip level, heterogeneous integration will evolve from advanced packaging to system-level design. At the architectural level, collaborative innovation will focus on overcoming the two fundamental bottlenecks: the memory wall and bandwidth wall. At the system level, heterogeneous computing power will be integrated into global resource networks, achieving service-oriented transformation. This evolution will occur not only within individual data centers but also in the coordinated scheduling of wide-area computing resources.

Computing Infrastructure: The Continuous Evolution from Servers to Rack-scale Computers

The exponential growth of AI computing demand—doubling every three to four months—has rendered traditional data center deployment models ("build first, deploy later") obsolete. To keep pace with GPU advancements, computing infrastructure in 2026 is shifting from discrete server units to highly integrated, plug-and-play rack-scale computers.

The Driving Force: Extreme Performance and Efficiency

With single AI training clusters approaching 50 megawatts and GPU power consumption exceeding 3,700 watts, the rack itself must evolve into a unified computing entity. Gone are stacks of individual servers—instead, compute "blades" and switch "trays" are integrated like CPU and memory modules on a motherboard. For example:

• NVIDIA's Rubin platform connects 144 GPU modules and switching chips via ultra-high-bandwidth backplanes, creating a deeply integrated "super node" within a single rack.
• Cloud giants like Google deploy server boards vertically (like DIMM slots) in immersion cooling tanks, revolutionizing both maintenance and compute deployment.

This transformation goes beyond hardware consolidation. It represents a fundamental shift in data center philosophy—from housing IT equipment to deep integration of building infrastructure, power, and networking. In this new paradigm, each high-density rack becomes a hot-swappable, intelligently orchestrated "compute cell", powering the limitless potential of future AI.

Cooling Revolution: Liquid Cooling Gains Significant Market Share

As single-chip TDP surpasses 1,000W, air cooling has hit its physical limits. Liquid cooling is rapidly transitioning from a premium option to an industry mandatory requirement. According to TrendForce, liquid cooling adoption for AI chips will reach 47% by 2026, with penetration in China's "East Data West Computing" hub nodes potentially hitting 65%.

The market will be divided among three dominant technologies:

• Cold Plate remains mainstream (~70% share) but is evolving from liquid-to-air (L2A) to more efficient liquid-to-liquid (L2L) architectures.
• Immersion Cooling will grow to 25% share, primarily in HPC and financial AI where extreme power density is critical.
• Cutting-edge chip-level microfluidic cooling is entering pilot phases, with microchannels etched directly into silicon interposers to deliver coolant beneath heat-generating transistors.

By 2026, liquid cooling will further expand its market share, particularly in AI data centers, where it will replace air cooling as the dominant solution.

Energy Challenge: Meeting the Power Demands of GW-Scale Data Centers

The exponential growth of AI computing power is driving data center energy requirements into unprecedented territory. Global tech giants have begun planning gigawatt (GW)-scale data center campuses, with the first facilities expected to come online as early as 2026.

Key Transformations in Power Infrastructure:

• Voltage Revolution

  • Power delivery systems are shifting from traditional 54V DC architectures to 800V high-voltage DC (HVDC) to support megawatt-scale racks.

• Energy Strategy Shift

  • Transitioning from centralized fossil fuel reliance to distributed low-carbon models
  • By 2030, fossil fuels' share in global data center power supply is projected to drop from ~70% to below 30%, replaced by wind, solar, and nuclear energy.

• Energy Storage Evolution

  • Storage systems are being redefined—from "emergency backup" to "core energy infrastructure"
  • Medium- to long-duration storage solutions will see rapid adoption, with increasingly decentralized deployment models.

This overhaul reflects the industry's dual imperative: scaling capacity while achieving sustainability in the AI era.

Network Evolution: The Microsecond Race to Break Through the Bandwidth Wall

By 2026, network performance has become the critical bottleneck limiting computing efficiency in data centers. With AI training's sensitivity to latency shifting from milliseconds to microseconds, networking technology is undergoing a complete transition from electrical to optical solutions.

Three-Stage Optical Interconnect Advancement:

• 800G pluggable optical modules enter mass deployment
• Linear Drive Pluggable Optics (LPO) begins commercialization
• Co-Packaged Optics (CPO) emerges in cutting-edge applications Market forecast: Global shipments of 800G+ optical modules will surge from 24 million in 2025 to 63 million in 2026.

Breakthrough Photonic Technologies:

• Silicon Photonics (SiPh) and Thin-Film Lithium Niobate (TFLN) modulation technologies mature rapidly
• Reduce optical module power consumption from >12 pJ/bit to 6 pJ/bit

Network Architecture Transformation:

• Traditional three-tier Spine-Leaf architecture gives way to flatter "Optical Cube" designs
• Silicon photonic switches enable sub-1μs any-port-to-any-port switching
• AI training clusters achieve sub-1μs All-to-All latency

This photonic revolution represents not just an incremental upgrade, but a fundamental rearchitecture of data center networks to meet the demands of next-generation AI workloads.

Intelligent O&M: From Assistive Tool to Decision-Making Brain

By 2026, AI will completely redefine data center operations management. Faced with 30GB/second of operational data from a 100MW campus, human teams can no longer process such massive information flows.

AIOps systems are evolving from support tools to decision-making brains, achieving:

• 97% accuracy in predicting equipment failures (e.g., overheating) 15 minutes in advance through real-time digital twins simulating airflow, power, and thermal dynamics

• Three fundamental transformations:

  • Proactive prevention replacing reactive responses
  • Multi-agent collaborative ecosystems replacing monolithic architectures
  • Reinvented human-AI collaboration replacing traditional manual operations

By 2026, AIOps as a decision-making brain will no longer be optional—it will become the central nervous system ensuring stable, efficient, and green operations for hyper-scale, high-density data centers. This shift moves data centers from experience-dependent management to self-optimizing intelligence powered by continuous learning.

Looking Ahead

By 2026, data centers will undergo transformative evolution centered on AI computing demands, driving system-level innovation to overcome performance and efficiency limits. In our view, these next-generation facilities will transcend their traditional role as cost centers—transforming into self-optimizing, intelligently orchestrated compute-energy hubs that deeply integrate with power grids and computing networks. This fusion will establish a smart, resilient foundation for the era of ubiquitous connectivity.