Japan’s planned FugakuNEXT supercomputer is targeting operation around 2030 and aims to bring AI and high-performance computing (HPC) together for scientific research. RIKEN, Japan’s research institute, leads the project with Fujitsu and NVIDIA as development partners. “Zetta-scale” describes an ambitious AI-performance target—not a measured result from a completed machine—and the project’s figures use different precision and performance measures.
What Japan announced
Japan’s Ministry of Education, Culture, Sports, Science and Technology launched FugakuNEXT’s development and deployment project in January 2025, naming RIKEN as the implementing organization. FugakuNEXT is RIKEN’s codename for the planned successor to Supercomputer Fugaku. RIKEN targets operation around 2030.
Fujitsu and NVIDIA are joint development partners. RIKEN describes the project as an AI-HPC platform that will combine scientific simulation with AI rather than simply replace Fugaku with a faster version of the same kind of machine. RIKEN’s January 2025 project announcement and its August 2025 partner announcement outline the project and its targets.
What “zetta-scale” means in this announcement
The headline term needs qualification. RIKEN’s January 2025 announcement says FugakuNEXT aims for at least 50 EFLOPS of AI execution performance, while keeping zetta-scale peak AI performance in view. In August 2025, RIKEN described AI-oriented hardware performance exceeding 600 EFLOPS in sparse FP8 and said it expected FugakuNEXT to be the first zetta-scale system as an HPC supercomputer.
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These figures are not interchangeable. EFLOPS means a quintillion floating-point operations per second; the precision used and whether sparse operations are counted affect the result. The 600-plus EFLOPS figure is specifically for sparse FP8, a low-precision AI measure. It is not an FP64 result, nor is it a directly comparable measure of an application’s achieved speed. RIKEN’s figures are targets or expectations for a system in development, not independently verified performance from a completed machine. The May 2026 basic-design report provides further project detail.
How the system is intended to serve science
RIKEN’s central idea is to tightly couple simulation and AI in computational science. Its examples of “AI for Science” include generating and validating hypotheses with AI, automating code generation, and automating physical experiments. Researchers could use simulation and AI together to explore scientific questions, but the project targets do not establish how much any particular research task will accelerate.
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RIKEN also plans application co-design, open-source system software, and cloud access to project software, AI models, and applications before the physical system launches. The August 2025 announcement gives “virtual Fugaku” as an example. It does not name a cloud provider or specify public signup, access terms, or pricing.
Performance targets—and what they do not promise
RIKEN has described several different performance ambitions. Each is a target, and actual results will depend on the workload, software, algorithms, and numerical precision.
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| Project ambition | Meaning and qualification |
|---|---|
| More than 5–10 times effective performance for existing HPC applications | RIKEN’s 2025 target for existing HPC workloads; not a reported benchmark result. |
| At least 50 EFLOPS of AI execution performance, with zetta-scale peak AI performance in view | RIKEN’s January 2025 framing distinguishes execution performance from peak performance. |
| More than 600 EFLOPS in sparse FP8 | RIKEN’s August 2025 AI-oriented hardware figure. It uses sparse, low-precision FP8 operations and should not be compared directly with FP64 results. |
| Up to 100 times overall application performance | RIKEN’s August 2025 eventual goal for combining simulation and AI, within approximately the same 40 MW power constraint used during Fugaku development. It is not an achieved speedup or a confirmed final system-power specification. |
When comparing these numbers with another supercomputer announcement, check whether the figure is peak or effective/application performance, which precision and sparsity assumptions apply, whether it refers to AI or existing HPC work, and whether it is a target or a measured result.
Architecture, partners, and deployment
The proposed design is heterogeneous: a power-efficient CPU component intended to use software assets developed for Fugaku, paired with a bandwidth-oriented accelerator component. RIKEN’s partner materials assign Fujitsu a central role in CPU and overall system design, and NVIDIA responsibility for GPU infrastructure. The intent is to preserve useful continuity for HPC applications while adding AI-oriented acceleration.
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Fujitsu and NVIDIA undertook design work with RIKEN during 2025–February 2026, according to the basic-design report. RIKEN says the system will be deployed adjacent to its Kobe site. Its operational policy points to advanced cooling, renewable energy, lower-carbon operation, and further integration of HPC and quantum computing. Those plans do not establish a finalized data-center design or measured power consumption for FugakuNEXT.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.FugakuNEXT is not RIKYU
RIKEN named a separate AI-for-Science development supercomputer RIKYU in June 2026. RIKYU is a different system; its published specifications are not FugakuNEXT specifications. RIKEN’s RIKYU announcement identifies that separate project.
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