AI Infrastructure for Every Industry
From LLM training clusters to quantitative research pods — purpose-built configurations for the most demanding computational workloads.
Powering the Next Generation of AI
Training and serving large language models demands massive GPU memory, high-bandwidth interconnects, and clusters that scale to hundreds or thousands of accelerators — infrastructure that is increasingly difficult to secure through traditional channels.
LLM Training Clusters
Multi-node AMD Instinct clusters with InfiniBand fabric, optimised for distributed training frameworks (PyTorch FSDP, DeepSpeed).
AI Inference Platforms
High-memory MI325X instances for cost-effective model serving at scale.
Agentic AI Infrastructure
Low-latency, high-throughput environments for multi-agent AI systems and real-time decision engines.
Enterprise AI Environments
Private, single-tenant deployments with complete data isolation for regulated industries.
Compute Infrastructure for Alpha Generation
Quantitative strategies demand low-latency environments for execution, and increasingly, GPU acceleration for machine learning models — all within strict data governance requirements.
Quant Research Infrastructure
Alpha research environments and portfolio optimisation compute.
Risk Analytics Platform
Monte Carlo simulation engines, market risk analysis (VaR, CVaR), and stress testing infrastructure.
AI Trading Infrastructure
Real-time market data processing, ML model training and inference, and financial time-series forecasting.
Accelerating Discovery, from Genomics to Drug Development
Modern biomedical research — from genomic sequencing to molecular dynamics simulation to AI-powered drug discovery — generates datasets measured in petabytes and requires computing power that traditional research clusters cannot provide.
Drug Discovery Platforms
GPU-accelerated molecular docking, virtual screening, and protein folding simulations.
Genomics Computing
High-memory instances for whole-genome sequence analysis and variant calling.
Bioinformatics Workloads
Scalable GPU environments for RNA-seq, single-cell analysis, and multi-omics integration.
Medical AI Infrastructure
Private, compliant environments for training and deploying clinical AI models.
Key benefit — the MI300X's 192GB of HBM3 memory allows entire genomic datasets to fit within a single GPU's memory, eliminating the data-sharding bottlenecks that plague smaller-memory alternatives.
Simulation Infrastructure for the Physical World
Computational fluid dynamics (CFD), finite element analysis (FEA), and digital twin simulations require sustained, high-throughput computing — workloads that are both memory-intensive and computationally demanding.
Digital Twin Infrastructure
High-fidelity, real-time simulation environments for product development and operational monitoring.
CFD Simulation
GPU-accelerated CFD workflows for Ansys Fluent, OpenFOAM, and STAR-CCM+.
CAE Workloads
GPU-accelerated engineering analysis with AMD Instinct accelerators.
Industrial AI
Infrastructure for predictive maintenance models, quality control AI, and supply chain optimisation.
Accessible HPC for the Academic Community
University research groups face a persistent compute gap — institutional clusters are oversubscribed, cloud costs from hyperscalers strain grant budgets, and procurement cycles for on-premise hardware stretch across academic years.
Scientific Computing
Flexible, on-demand GPU resources for grant-funded research projects.
Academic Research Clusters
Multi-user HPC environments with job scheduling (Slurm) and shared storage.
Shared HPC Environments
Multi-tenant platforms enabling cross-institutional collaboration.
Collaborative Research Platforms
Dedicated environments for multi-university research consortia.
Grant-aligned billing — flexible commitment terms that match academic funding cycles. No multi-year lock-in required.
Not Sure Which Configuration Fits Your Workload?
Our solutions team can assess your requirements and recommend the optimal infrastructure — no obligation.
Schedule a Free Consultation