Intelligent Compute to Hit 9,800 EFLOPS by 2030: Five Hard Targets in the ICT Industry "15th Five-Year" Plan
This article is based on the MIIT "15th Five-Year" Plan for ICT Industry Development (issued in September 2026) and official interpretations by the People's Post and Telegraph News, The Paper, Huaxia Times, and other outlets.
In early September, the MIIT issued the "15th Five-Year" Plan for ICT Industry Development, drawing a roadmap for the ICT industry over the next five years with 13 major indicators and 26 key tasks. For the AI compute industry, this is the most substantive policy document — we have picked out five hard targets and break down their industry implications one by one.
Target 1: Intelligent Compute from 1,590 to 9,800 EFLOPS, More Than 5x Growth in Five Years
This is the standout number in the entire plan. For reference:
- As of the end of June 2026, national intelligent compute capacity had reached 2,185 EFLOPS, up 177% year over year — the first half alone overshot the year-end 2025 target (1,590 EFLOPS)
- The country has built 42 10,000-card-class intelligent computing clusters; in July 2026, the first fully domestic 100,000-card-class AI compute cluster, the Sugon 8000, was completed in Zhengzhou
- Huawei's rotating chairman Wang Tao's assessment: super-node clusters of more than 100,000 cards will become basic configuration by 2027
Going from 2,185 to 9,800 means that over the next four years the country must build 3.5 times the current installed base of intelligent computing facilities. The plan also calls for "orderly deployment of 10,000-card, 100,000-card and larger intelligent computing clusters" and "stepping up efforts to adapt domestic compute chips" — this is the most certain demand base for domestic AI chips over the next five years.
Target 2: Cumulative Information Infrastructure Investment of RMB 3.8 Trillion
Some have compared this with the "14th Five-Year" figure of RMB 3.7 trillion and concluded that "investment has peaked." Official interpretations explicitly reject this claim: incremental capital is shifting from "scale expansion" to "quality-and-efficiency gains," precisely targeted at intelligent computing infrastructure, 10G optical networks, 6G, and other new tracks. The industry chain pull effect is changing accordingly — AI servers, high-speed optical interconnects, domestic compute chips, and new smart terminals were named as beneficiaries across the whole chain.
Target 3: PUE of New Large Compute Facilities Reduced Below 1.2
At the end of the "14th Five-Year" period this figure was 1.25, and it must fall another 0.05 within five years — it doesn't sound like much, but with AI servers running at high load year-round and per-rack power density generally exceeding 20kW, every point of PUE reduction is a hard fight. The technical path laid out in the plan is very clear: liquid cooling.
- The "15th Five-Year" Plan for ICT Industry Development: guide compute facilities to adopt high-efficiency energy-saving equipment and advanced technologies such as liquid cooling
- The "15th Five-Year" Plan for Electronic Information Manufacturing Development (jointly issued by the MIIT and the NDRC on September 15): lists liquid cooling alongside high-bandwidth memory pooling and all-optical switching as key technologies to be broken through
Industry-side data confirms the trend: according to Omdia, liquid cooling's share of the global data center cooling market climbed from about 20% in 2024 to about 37% in 2025, with penetration expected to exceed 45% by 2029. The ceiling on compute expansion is shifting from chip supply to power supply, and the energy-efficiency constraint of "compute up, energy down" will be the norm for the next five years.
Target 4: Advanced Storage Capacity of 1,700 EB, More Than 2x Growth
While intelligent compute grows 5x, advanced storage grows more than 2x — storage-compute coordination is given equal weight. The logic: training checkpoints for large models, and the intermediate states and contextual memory of agent inference, are all stored in layers within high-performance storage. As of the end of June, national storage capacity totaled about 2,021 EB, of which advanced storage accounted for about 32%; the 1,700 EB advanced storage target for 2030 means structural upgrading matters more than total-volume growth.
A direct implication for chip selection: KV cache and long context are eating the memory budget — when evaluating AI servers, memory capacity and memory bandwidth should carry more weight than peak compute.
Target 5: The Agent Interconnection Network Enters the Plan for the First Time
This is the most forward-looking part of the plan: the "agent interconnection network" gets its own dedicated column, deploying four areas of work — building an agent network identifier system, accelerating the construction and application of agent network infrastructure, promoting global interconnection, and establishing a space governance system. It also explicitly states "launching 6G commercial use in a timely manner."
As AI shifts from "applications for people" to "agent infrastructure," the role of the communications network upgrades from connecting people to connecting agents — providing a national-level narrative for the distributed deployment of inference compute (edge inference, compute scheduling).
Three Judgments for Compute Practitioners
| Judgment | Basis |
|---|---|
| Domestic compute demand is highly certain | The plan explicitly states "stepping up efforts to adapt domestic compute chips" + the 100,000-card cluster build cycle has begun |
| Energy-efficiency targets become hard siting constraints | PUE below 1.2 + liquid cooling named as a key technology; high-density liquid cooling solutions take priority |
| Inference compute sinks to the edge on demand | "Deploy inference compute facilities as needed for scenarios"; edge and regional compute centers enjoy a policy window |
Under a 5x compute expansion target, what has always been scarce is not planning but chips, power, and delivery capability. For buyers, the supply window remains tight; for solution selection, we recommend using the TCO Calculator to convert PUE differences into electricity costs — the gap between 1.25 and 1.2 amounts to tens of millions on the electricity bill of a 10,000-card cluster.
Summary
The "15th Five-Year" Plan writes intelligent compute into a national-level project: 5x compute, 2x storage, RMB 3.8 trillion in investment, PUE 1.2, and the agent internet. Looking back five years from now, the wave of domestic 100,000-card clusters in the second half of 2026 may well prove to be the starting point of this curve.
(Plan data is cited from MIIT documents and official media interpretations; market data is cited from statistics by CAICT, Omdia, and other institutions.)