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2026 Global AI Computing Report & Ten Major Computing Industry Trends Released

· 7 min read
AI Compute Cards Wiki Editorial
Industry Research Team

On May 29, 2026, during the World Intelligence Expo 2026 in Tianjin, the China Intelligent Computing Industry Alliance, National Supercomputing Center in Tianjin, Tianjin Artificial Intelligence Society, Shenzhen Artificial Intelligence Industry Association,ZDNET, and ZDNET ThinkTank jointly released the "2026 Global AI Computing Development Research Report."

The report analyzes the current state and future trends of the global AI computing industry, revealing that the sector has entered a new stage of "intelligence-driven, system-reconstruction."

Core Viewpoints​

1. Computing power becomes a national strategic element​

The global computing industry is entering a new stage of "intelligence-driven, system-reconstruction." With the rise of the "token economy," computing power has become a key foundational element supporting national technological breakthroughs, industrial competition, and strategic positioning.

Computing is evolving from traditional IT support into a strategic bedrock driving scientific innovation and the industrial revolution.

2. AI computing development covers the full chain​

AI computing development must upgrade the full chain of chip, system, and compute cluster, while matching the differentiated computing needs of model training, inference, and data preparation.

  • Training: pre-training of super-large models needs ten-thousand-card-scale compute
  • Inference: super-large models need thousand-card-scale compute
  • Data preparation: needs tens to hundreds of cards

Compute demand at both training and inference ends will keep growing.

3. Domestic AI chip industry's distinctive path​

The domestic AI chip industry follows a route of "autonomy + cluster breakthrough + hardware-software integration + cost-performance advantage," distinct from the foreign pursuit of absolute single-chip compute — better suited to large-scale deployment.

4. Energy challenges for computing centers and solutions​

Computing centers have become the fastest-growing source of global electricity demand. The future requires a diversified energy supply of "short-term wind-solar-storage integration, mid-term nuclear, long-term hydrogen."

Meanwhile, space computing will become a new direction to solve ground-based computing bottlenecks.

5. Compute-network convergence as a core direction​

Future computing will move toward "compute-network convergence," making compute as on-demand as water and electricity — a core part of the national modern infrastructure system.

The computing network has been included in the national "15th Five-Year Plan" major engineering projects, ranked alongside public infrastructure such as hydro power.

Key Data​

Compute performance evolution​

MetricEvolution trend
Chip computefrom TFLOPS scale up to tens of PFLOPS
System formfrom single 8-card machine to thousand-card super-node architecture
Cluster scalefrom thousand-card clusters to hundreds-of-thousands-card clusters
Cluster powerfrom kilowatt to gigawatt scale

Global computing center capacity & energy forecast​

  • Global computing center total capacity: expected to grow from 102GW (2026) to 220GW (2030)

    • AI load capacity from 62GW to 156GW, share rising to 71%

  • U.S. computing center annual electricity: expected to grow from 292TWh to 606TWh, share of national demand rising to 11%

  • China computing center total capacity: ~60GW by 2030, AI load share rising to 48%

  • Global computing center electricity: per IEA base scenario, from ~415TWh (2024) to ~945TWh (2030), ~15% CAGR

Embodied intelligence compute support data​

  • Cloud compute: can generate PB-scale interaction data daily; large-model training cycle shortened from months to weeks
  • Edge compute: tens-to-hundreds of TOPS enables 10–50ms low-latency real-time perception & decision

Industry Trend Analysis​

1. Heterogeneous architecture upgrade​

From traditional CPU+GPU to a new GPU+LPU+CPU+DPU heterogeneous inference architecture.

CPU plays the core role of task scheduling, data pre-processing, serial tasks, and system interconnection in heterogeneous architectures. In 2010, "Tianhe-1A" pioneered large-scale CPU+GPU deployment, leading the global intelligent-computing underlying architecture direction.

2. Clear scale-up / scale-out paths​

  • Scale Up: pursue extreme performance by raising single-node hardware config
  • Scale Out: add nodes for load sharing and high availability

Together they form the core support of computing system capability.

3. Super-node servers become mainstream​

With ultra-high interconnect bandwidth and low communication latency, they shorten model training cycles.

Representative products:

  • Huawei Ascend 384 super-node
  • Sugon scaleX640 super-node
  • Alibaba Cloud Panjiu AL128 super-node
  • Inspur YuanNao SD200
  • Kunlunxin super-node solution

4. Long-context processing optimization​

Through Compressed Sparse Attention (CSA), Heavy-Compressed Attention (HCA) and sliding-window mechanisms, build a "coarse + fine, sparse + dense" long-context modeling system to improve compute efficiency.

Representative application: DeepSeek-V4 attention architecture design.

AI chips​

International vendors:

  • NVIDIA: leads high-end training/inference with Blackwell and Rubin architectures
    • GTC 2026 Taipei (June 1) major releases:
      • Vera Rubin platform in full mass production: NVL72 rack system, agent throughput 10x over Grace Blackwell
      • Vera CPU released: 88-core Olympus in-house Armv9.2, LPDDR5X 1.5TB, 1.2 TB/s, world's first CPU with native FP8
      • RTX Spark AI PC chip: co-developed with MediaTek and Microsoft (codename N1X), Blackwell GPU 1 PFLOP, 128GB unified memory, TSMC 3nm
      • Nemotron 3 Ultra open model: SSM+MoE hybrid, 5x inference speed, 30% lower cost
    • Expanding advantage via CUDA ecosystem
  • Google: deepens vertical HW/SW integration via in-house TPU
  • AWS: Trainium (training) + Inferentia (inference) for cost-effective cloud compute

Domestic vendors: a product matrix represented by Huawei Ascend 910C, Kunlunxin P800, Moore Threads MTT S5000, MetaX XiYun C600.

In 2026 Huawei proposed the "Tao (τ) Law," aiming to systematically reduce the time constant and raise transistor density via logic folding, driving domestic chip evolution.

AI workstations​

  • Form factors: tower, mobile, mini — for different deployment scenarios
  • Compute tiers: entry, professional, enterprise — covering personal dev to enterprise deployment

AI servers​

  • By function: training AI servers and inference AI servers
  • By deployment: cloud AI servers and edge AI servers

With high compute output, high memory bandwidth, and high-speed interconnect, suited to large-scale parallel tasks.

AI computing centers​

  • Trending toward "high AI share, high power density, high electricity consumption"
  • Ultra-large AI computing centers become the construction focus
  • Energy supply moving toward diversified clean sources

Space computing is a new direction, leveraging space's continuous sunlight, extreme cold/vacuum, and interference-free environment to solve ground centers' energy, cooling, and interconnect bottlenecks. Starcloud and Guoxing Weiyu have begun exploration.

1. Scientific research paradigm shift​

The "dry-wet closed loop" research paradigm becomes mainstream, forming a loop between AI-driven "dry experiments" and automated "wet experiments" via data feedback — shifting science from experience-driven to model-driven.

2. Synthetic biology empowerment​

AI's multi-task learning and unknown-space exploration can decode biology's complex "sequence–structure–function" mapping, enabling breakthroughs in protein synthesis, gene editing, and nucleic-acid vaccines. E.g., the AlphaFold series revolutionized protein structure prediction.

3. Embodied intelligence support​

Efficient cloud-edge compute coordination provides full-stack support for embodied intelligence — covering massive data processing, high-fidelity simulation, model training, and edge real-time perception/decision in a closed loop.

Compute-network convergence is the core direction, evolving from "interconnect first, then network" toward a national integrated computing network. The three major telecom operators have begun interconnecting their own compute with dispersed social compute nationwide, promoting ubiquitous compute supply.

The domestic computing ecosystem keeps improving, with deeper government-industry-academia-research coordination. The China Intelligent Computing Industry Alliance, National Supercomputing Center in Tianjin, regional AI societies, industry associations, and service institutions jointly build exchange platforms — driving R&D, standard-setting, technology transfer, and talent cultivation for high-quality domestic computing development.

Conclusions & Outlook​

  1. Computing power is a core element of national strategic competitiveness — major countries are increasing infrastructure investment to seize the AI-era high ground.
  2. The domestic AI chip industry follows a distinctive path — via cluster breakthrough, HW/SW integration, and cost-performance, forming advantage in large-scale deployment.
  3. Computing architecture keeps evolving — heterogeneous computing, super-node servers, and long-context processing are key directions.
  4. Application scenarios keep expanding — from research paradigm shifts to synthetic biology and embodied intelligence, AI compute deeply empowers frontier fields.
  5. Computing infrastructure evolves toward compute-network convergence — future compute will be ubiquitous public infrastructure, on-demand like water and electricity.

References:

  • "2026 Global AI Computing Development Research Report" (China Intelligent Computing Industry Alliance et al.)
  • World Intelligence Expo 2026 (Tianjin, May 29, 2026)