# LEONARDO: A Pan-European Pre-Exascale Supercomputer for HPC and AI Applications

arXiv: https://arxiv.org/abs/2307.16885 · HTML: https://mlsanity.com/paper/2307.16885/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2023.

## Built on by (most cited first, 12 of 12)

- [High-Resolution Probabilistic Data-Driven Weather Modeling with a Stretched-Grid](https://mlsanity.com/paper/2511.23043/) (2025)
- [Exploring GPU-to-GPU Communication: Insights into Supercomputer Interconnects](https://mlsanity.com/paper/2408.14090/) (2024)
- [Designing Role Vectors to Improve LLM Inference Behaviour](https://mlsanity.com/paper/2502.12055/) (2025)
- [MoSE: Hierarchical Self-Distillation Enhances Early Layer Embeddings](https://mlsanity.com/paper/2503.03008/) (2025)
- [Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators](https://mlsanity.com/paper/2505.14039/) (2025)
- [Modalities, a PyTorch-native Framework For Large-scale LLM Training and Research](https://mlsanity.com/paper/2602.08387/) (2026)
- [Bine Trees: Enhancing Collective Operations by Optimizing Communication Locality](https://mlsanity.com/paper/2508.17311/) (2025)
- [Swedish Whispers; Leveraging a Massive Speech Corpus for Swedish Speech Recognition](https://mlsanity.com/paper/2505.17538/) (2025)
- [VOX-KRIKRI: Unifying Speech and Language through Continuous Fusion](https://mlsanity.com/paper/2509.15667/) (2025)
- [Multimodal Large Language Models for Low-Resource Languages: A Case Study for Basque](https://mlsanity.com/paper/2511.09396/) (2025)
- [Generative Nested Sampling of Atomistic Thermodynamic Landscapes](https://mlsanity.com/paper/2609.03193/) (2026)
- [CodeGraph: Open-Taxonomy Knowledge Graph for Source Code with Wikidata Grounding](https://mlsanity.com/paper/2609.29474/) (2026)

## Abstract (arXiv)

A new pre-exascale computer cluster has been designed to foster scientific progress and competitive innovation across European research systems, it is called LEONARDO. This paper describes the general architecture of the system and focuses on the technologies adopted for its GPU-accelerated partition. High density processing elements, fast data movement capabilities and mature software stack collections allow the machine to run intensive workloads in a flexible and scalable way. Scientific applications from traditional High Performance Computing (HPC) as well as emerging Artificial Intelligence (AI) domains can benefit from this large apparatus in terms of time and energy to solution.