# Isambard-AI: a leadership class supercomputer optimised specifically for Artificial Intelligence

arXiv: https://arxiv.org/abs/2410.11199 · HTML: https://mlsanity.com/paper/2410.11199/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 12 references that are in the map.
- First posted on arXiv 15 Oct 2024.

## Builds on (most cited first, 1 of 1)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)

## Built on by (most cited first, 30 of 62)

- [Meta Flow Maps enable scalable reward alignment](https://mlsanity.com/paper/2601.14430/) (2026)
- [V-DPM: 4D Video Reconstruction with Dynamic Point Maps](https://mlsanity.com/paper/2601.09499/) (2026)
- [Multilingual Language Model Pretraining using Machine-translated Data](https://mlsanity.com/paper/2502.13252/) (2025)
- [A versatile foundation model for cine cardiac magnetic resonance image analysis tasks](https://mlsanity.com/paper/2506.00679/) (2025)
- [MACE-POLAR-1: A Polarisable Electrostatic Foundation Model for Molecular Chemistry](https://mlsanity.com/paper/2602.19411/) (2026)
- [Kalman Linear Attention: Parallel Bayesian Filtering For Efficient Language Modelling and State Tracking](https://mlsanity.com/paper/2602.10743/) (2026)
- [RAVEN: Real-time Autoregressive Video Extrapolation with Consistency-model GRPO](https://mlsanity.com/paper/2605.15190/) (2026)
- [Multilingual Pretraining Using a Large Corpus Machine-Translated from a Single Source Language](https://mlsanity.com/paper/2410.23956/) (2024)
- [TESSERA v2: Scaling Pixel-wise Earth Foundation Models](https://mlsanity.com/paper/2607.03949/) (2026)
- [CountLoop: Training-Free High-Instance Image Generation via Iterative Agent Guidance](https://mlsanity.com/paper/2508.16644/) (2025)
- [Donors and Recipients: On Asymmetric Transfer Across Tasks and Languages with Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2511.13368/) (2025)
- [LCMem: A Universal Model for Robust Image Memorization Detection](https://mlsanity.com/paper/2512.14421/) (2025)
- [Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks](https://mlsanity.com/paper/2601.03448/) (2026)
- [Can LLMs Compress (and Decompress)? Evaluating Code Understanding and Execution via Invertibility](https://mlsanity.com/paper/2601.13398/) (2026)
- [MaDiS: Taming Masked Diffusion Language Models for Sign Language Generation](https://mlsanity.com/paper/2601.19577/) (2026)
- [VDAWorld: World Modelling via VLM-Directed Abstraction and Simulation](https://mlsanity.com/paper/2512.11061/) (2025)
- [Efficient Camera-Controlled Video Generation of Static Scenes via Sparse Diffusion and 3D Rendering](https://mlsanity.com/paper/2601.09697/) (2026)
- [Metropolis-Adjusted Diffusion Models](https://mlsanity.com/paper/2605.09654/) (2026)
- [Enhanced Neural Video Representation Compression Across Extreme Complexity and Quality Scales](https://mlsanity.com/paper/2606.28163/) (2026)
- [Video, How Do Your Tokens Merge?](https://mlsanity.com/paper/2506.03885/) (2025)
- [Retrieval-augmented reasoning with lean language models](https://mlsanity.com/paper/2508.11386/) (2025)
- [Lost in Space? Vision-Language Models Struggle with Relative Camera Pose Estimation](https://mlsanity.com/paper/2601.22228/) (2026)
- [Consistency Regularised Gradient Flows for Inverse Problems](https://mlsanity.com/paper/2605.07907/) (2026)
- [Exact Posterior Score Estimation for Solving Linear Inverse Problems](https://mlsanity.com/paper/2606.17048/) (2026)
- [Scaling Generative Foundation Models for Chest Radiography with Rectified Flow Transformers](https://mlsanity.com/paper/2606.19460/) (2026)
- [Instruction-Conditioned Exploration for Reinforcement Learning with Self-Distillation to an Unconditioned Policy](https://mlsanity.com/paper/2608.02087/) (2026)
- [Softplus Attention with Re-weighting Boosts Length Extrapolation in Large Language Models](https://mlsanity.com/paper/2501.13428/) (2025)
- [Compliance vs. Sensibility: On the Reasoning Controllability in Large Language Models](https://mlsanity.com/paper/2604.27251/) (2026)
- [The Learnability Gap in Medical Latent Diffusion](https://mlsanity.com/paper/2605.17087/) (2026)
- [Geometry-Aware Uncertainty Coresets for Robust Visual In-Context Learning in Histopathology](https://mlsanity.com/paper/2605.18419/) (2026)

## Abstract (arXiv)

Isambard-AI is a new, leadership-class supercomputer, designed to support AI-related research. Based on the HPE Cray EX4000 system, and housed in a new, energy efficient Modular Data Centre in Bristol, UK, Isambard-AI employs 5,448 NVIDIA Grace-Hopper GPUs to deliver over 21 ExaFLOP/s of 8-bit floating point performance for LLM training, and over 250 PetaFLOP/s of 64-bit performance, for under 5MW. Isambard-AI integrates two, all-flash storage systems: a 20 PiByte Cray ClusterStor and a 3.5 PiByte VAST solution. Combined these give Isambard-AI flexibility for training, inference and secure data accesses and sharing. But it is the software stack where Isambard-AI will be most different from traditional HPC systems. Isambard-AI is designed to support users who may have been using GPUs in the cloud, and so access will more typically be via Jupyter notebooks, MLOps, or other web-based, interactive interfaces, rather than the approach used on traditional supercomputers of sshing into a system before submitting jobs to a batch scheduler. Its stack is designed to be quickly and regularly upgraded to keep pace with the rapid evolution of AI software, with full support for containers. Phase 1 of Isambard-AI is due online in May/June 2024, with the full system expected in production by the end of the year.