Enterprise AI Analysis
Unlocking Enterprise AI Potential
Isambard-AI represents a monumental leap in AI-capable supercomputing, designed to accelerate UK's research capabilities. Leveraging NVIDIA Grace-Hopper GPUs within HPE Cray EX4000 systems in a bespoke Modular Data Centre, it delivers unprecedented performance for LLM training and general AI workloads. Its rapid deployment, energy efficiency, and cloud-native software ecosystem set a new standard for AI infrastructure, making advanced AI accessible to a broad range of users and supporting critical research in AI safety and trustworthiness.
Key Performance Indicators
Isambard-AI pushes the boundaries of what's possible in large-scale AI research and deployment.
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ExaFLOP/s (8-bit)
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NVIDIA GH200 GPUs
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Target PUE
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Months to Full Deployment
Deep Analysis & Enterprise Applications
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Isambard-AI is built upon a HPE Cray EX4000 system housed in a highly energy-efficient Modular Data Centre (MDC). This direct liquid-cooled (DLC) solution is designed for unprecedented power density and a target Power Usage Effectiveness (PUE) of less than 1.1. The system features 5,448 NVIDIA Grace-Hopper GPUs, delivering over 21 ExaFLOP/s of 8-bit floating-point performance.
Its architecture allows for rapid deployment—from concept to operational in less than a year—and provides a sustainable infrastructure with over 90% recyclability of materials. The hybrid MDC supports both air-cooled Grace-Grace and DLC Grace-Hopper systems.
Designed with a cloud-native approach, Isambard-AI supports a broad range of users through interactive interfaces like Jupyter Notebooks and MLOps platforms, moving away from traditional HPC batch scheduling. The software stack is designed for rapid and regular upgrades, supporting containerized applications.
It integrates HPE's Cray Systems Management (CSM), a Kubernetes-based solution, to provide a multi-tenancy architecture with infrastructure-as-code and DevOps practices. This enables resource elasticity and efficient deployment of custom IaaS, PaaS, and SaaS solutions for AI/ML frameworks.
Isambard-AI integrates two distinct all-flash storage systems: a 20 PiByte Cray ClusterStor E1000 based on Lustre for high-bandwidth and IOPs, and a 3.5 PiByte VAST Data Platform for multi-protocol, multi-tenant SDS capabilities.
These solutions provide flexibility for training, inference, and secure data access, supporting diverse data requirements of AI/ML payloads. The system also includes a programmable, API-driven Data Management Framework (DMF) to facilitate data movement, staging, archiving, and integration with other national facilities and cloud object storage.
21 ExaFLOP/s
8-bit AI Performance for LLM Training
Enterprise Process Flow
Contract Signature
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Site Preparation (2-3 Months)
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MDC Delivery & Assembly
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IT Installation & Integration
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Phase 1 Online (May/June 2024)
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Full System Production (End of 2024)
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Impact on UK AI Research
Isambard-AI represents a 30X increase in AI performance capabilities for the UK, making it a pivotal resource for open research and scientific discovery. Before its deployment, GPU-enabled systems in the UK were limited to a few hundred previous-generation GPUs. This new infrastructure will directly support critical research in AI safety and trustworthiness, a key mission area for the UK government. The rapid deployment model, moving from zero to operational in less than a year, demonstrates a new paradigm for building national-scale AI infrastructure.
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Estimated Annual Savings
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Hours Reclaimed Annually
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Your AI Transformation Roadmap
A typical phased approach to integrate Isambard-AI's capabilities into your enterprise.
Phase 1: Discovery & Strategy (2-4 Weeks)
Comprehensive assessment of current infrastructure, data readiness, and business objectives. Develop a tailored AI strategy and roadmap aligned with Isambard-AI's capabilities.
Phase 2: Pilot & Integration (8-12 Weeks)
Execute a pilot project on Isambard-AI, integrate initial models, and establish secure data pipelines. Test performance and validate ROI metrics with real-world data.
Phase 3: Scalable Deployment (12-24 Weeks)
Full-scale deployment of AI solutions, optimizing for performance and cost-efficiency. Implement MLOps pipelines and user-friendly interfaces for broader adoption.
Phase 4: Continuous Optimization (Ongoing)
Establish monitoring, continuous improvement, and model retraining cycles. Explore advanced features and expand AI applications across the enterprise.
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