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.

The UK government has announced a $273 million investment to build its most powerful supercomputer yet, Isambard-AI, which will rank among the top AI supercomputers in the world when it's switched on. It'll pack thousands of NVIDIA superchips, allowing it to run more than 200 quadrillion calculations per second. Isambard-AI is expected to begin operations in summer 2024 and will be hosted by the University of Bristol. The supercomputer is being built by Hewlett Packard Enterprise and will use 5,448 of NVIDIA's GH200 Grace Hopper Superchips, NVIDIA said in its own announcement. It'll be able to achieve over 21 exaflops of AI performance, or over 21 quintillion floating point operations per second for AI applications, like training large language models.