AMD Instinct and EPYC Power Europe’s Next LUMI-AI Supercomputer
Europe is moving ahead with its next-generation AI and high-performance computing infrastructure.
The EuroHPC Joint Undertaking (EuroHPC JU) has signed a €387.8 million procurement contract with Bull to deploy LUMI-AI at the CSC – IT Center for Science data center in Kajaani, Finland. Deployment is scheduled to begin in the second half of 2027.
As the successor to the existing LUMI supercomputer, LUMI-AI is designed to deliver approximately 10× the AI capacity of the current system while nearly doubling its conventional HPC performance.
The platform will combine AMD Instinct MI430X accelerators with 6th Gen AMD EPYC processors, creating a heterogeneous infrastructure designed for both AI and traditional scientific computing.
🌍 LUMI-AI Becomes the Compute Core of Europe’s AI Factory #
LUMI-AI will serve as the compute core of the LUMI AI Factory, a European initiative intended to make advanced AI and supercomputing resources more accessible to researchers, startups, and industry.
The LUMI AI Factory consortium includes:
- Finland
- Czech Republic
- Denmark
- Estonia
- Norway
- Poland
The total procurement contract is valued at €387.8 million, covering the system’s procurement, delivery, installation, and full lifecycle maintenance.
Funding is split equally between EuroHPC JU, through the Digital Europe Programme, and participating consortium members.
LUMI-AI is therefore more than a conventional supercomputer replacement. It is being positioned as a shared European infrastructure layer for AI development, scientific discovery, and industrial innovation.
🚀 10× More AI Capacity, Nearly 2× HPC Performance #
The headline performance target is a major increase in AI capability.
Compared with the current LUMI system, the new platform is expected to provide:
| Metric | LUMI-AI Target |
|---|---|
| AI capacity | ~10× LUMI |
| HPC performance | Nearly 2× LUMI |
| Deployment | H2 2027 |
| Contract value | €387.8 million |
| Location | Kajaani, Finland |
The system will target a broad range of workloads rather than optimizing exclusively for AI.
Expected applications include:
- Large-scale AI training
- AI inference
- Fine-tuning and scientific AI
- Industrial AI development
- Academic research
- Scientific simulations
- Data-intensive modeling
- High-performance computing
This workload diversity is central to LUMI-AI’s architecture.
🧠 AMD Instinct MI430X Brings HBM4 to HPC and AI #
At the accelerator level, LUMI-AI will use AMD Instinct MI430X GPUs, AMD’s accelerator platform for demanding HPC and AI workloads.
The MI430X is based on AMD’s CDNA 5 architecture and is specified with:
| MI430X Specification | Value |
|---|---|
| Architecture | CDNA 5 |
| HBM | 432 GB HBM4 |
| Peak FP64 | 288 TFLOPS |
| Target workloads | AI + HPC |
| Software ecosystem | AMD ROCm |
The combination of 432 GB of HBM4 and high FP64 throughput is particularly relevant to scientific computing.
Traditional AI accelerators often emphasize lower-precision matrix operations for neural-network workloads. Scientific simulations, however, can require much higher numerical precision.
The MI430X is therefore intended to support both sides of the workload spectrum:
LUMI-AI
│
┌─────────┴─────────┐
│ │
AI workloads HPC workloads
│ │
Training / Inference Simulation
Fine-tuning Modeling
Data analysis Scientific computing
│ │
└─────────┬─────────┘
│
MI430X + HBM4
This convergence is one of the defining characteristics of the new system.
⚙️ 6th Gen EPYC Venice Provides 256-Core CPU Compute #
The accelerator layer will be paired with 6th Gen AMD EPYC processors, codenamed Venice and based on the Zen 6 architecture.
Flagship configurations can reach 256 CPU cores, providing the general-purpose processing capacity needed to coordinate accelerators, handle operating-system workloads, process data, and execute CPU-oriented HPC tasks.
A simplified division of responsibilities looks like this:
6th Gen EPYC CPUs
│
├── General-purpose computation
├── Data preparation
├── System orchestration
└── CPU-side HPC workloads
│
▼
AMD Instinct MI430X
│
┌───────────┴───────────┐
│ │
AI acceleration HPC acceleration
│ │
Training / inference Scientific simulation
The combination gives LUMI-AI a heterogeneous compute platform instead of treating CPUs and GPUs as independent resources.
💧 BullSequana XH3500 Handles the Thermal Challenge #
A system of this scale and density creates a substantial cooling challenge.
LUMI-AI will use Bull’s BullSequana XH3500 liquid-cooling architecture, allowing the system to remove heat directly from high-density computing hardware.
Liquid cooling is increasingly important for modern AI and HPC systems because accelerator and CPU power densities continue to rise.
The LUMI-AI deployment will take place at the existing CSC data center in Kajaani, Finland, extending Europe’s supercomputing infrastructure at a location already associated with large-scale scientific computing.
🔬 AI and HPC Converge in One Infrastructure #
One of LUMI-AI’s most important design goals is workload convergence.
Instead of building separate infrastructures for AI training and traditional scientific simulation, the system is intended to support both within the same computing environment.
This matters because AI is increasingly becoming part of scientific workflows.
A modern research pipeline may look like:
Scientific data
│
▼
Simulation ───────────────┐
│ │
▼ │
Large-scale dataset │
│ │
▼ │
AI model training │
│ │
▼ │
Prediction / analysis ◄───┘
│
▼
New scientific insight
For example, AI models can analyze simulation results, accelerate portions of scientific workloads, identify patterns in experimental data, or provide surrogate models for computationally expensive simulations.
LUMI-AI is designed around this increasingly blurred boundary between HPC computation, AI training, inference, and data analysis.
🌐 Building European AI Sovereignty #
The project also has a strategic dimension.
Europe’s demand for AI compute is growing rapidly, but access to large-scale accelerator infrastructure remains concentrated among a relatively small number of global cloud and technology providers.
By investing in publicly accessible supercomputing infrastructure, EuroHPC and its member states can provide researchers and European companies with access to large-scale AI resources within the European research ecosystem.
LUMI-AI is particularly significant because it combines:
- Large-scale AI acceleration
- High-precision scientific computing
- High-capacity accelerator memory
- Open software through AMD ROCm
- European research infrastructure
- Industrial AI access
This makes the system part of a broader effort to build an AI ecosystem around European supercomputing resources.
📅 Deployment Begins in the Second Half of 2027 #
Under the current procurement plan, deployment of LUMI-AI is scheduled to begin in the second half of 2027.
Once deployed, access will be opened progressively to eligible European researchers and industry users.
The transition from LUMI to LUMI-AI therefore represents more than a generational performance upgrade. It marks a shift toward supercomputers that are designed from the beginning around the convergence of AI and traditional HPC.
With AMD’s MI430X accelerators supplying high-capacity HBM4 and AI/HPC compute, alongside 256-core Zen 6 EPYC processors and liquid-cooled Bull infrastructure, LUMI-AI is positioned to become a major European platform for scientific AI and large-scale computing.
🔮 LUMI-AI Points Toward the Future of Supercomputing #
The architecture reflects a broader change across the supercomputing industry.
The traditional distinction between a supercomputer for scientific simulation and a data center for AI is becoming increasingly difficult to maintain. AI is now embedded in scientific research, while HPC workloads increasingly rely on machine learning and data-driven methods.
LUMI-AI addresses that convergence directly:
Traditional HPC
│
│
▼
┌───────────────┐
│ LUMI-AI │
│ │
│ EPYC + MI430X│
│ + HBM4 │
└───────────────┘
▲
│
│
AI / ML
If its deployment targets are achieved, LUMI-AI will give Europe a substantially larger platform for both AI and scientific computing while extending the capabilities of the existing LUMI ecosystem.
The key idea is not simply more GPU performance. It is the integration of AI acceleration, high-precision HPC, large-capacity memory, and general-purpose CPU computing into a single European-scale infrastructure.