Workload
Engineering simulation
CFD, FEA, optimisation and design-of-experiments with CPU, memory, GPU and storage sized from representative cases.
Hyperion application planning
Application labels are only a route into discovery. Representative jobs, data, concurrency, latency, licences and deployment constraints provide the engineering basis.
Application routes
No generic application label guarantees performance or suitability. Formal quotation follows technical review.
Workload
CFD, FEA, optimisation and design-of-experiments with CPU, memory, GPU and storage sized from representative cases.
Workload
Professional GPU platforms for model development, fine-tuning and evaluation with dataset, framework and VRAM constraints explicit.
Workload
On-premise or controlled serving architectures shaped around model size, concurrency, latency, data boundary and operations.
Workload
Memory-, CPU-, storage- and GPU-aware platforms for research pipelines, imaging and sequence workloads.
Workload
Technical compute for approved research workflows where deployment, access, data governance and validation remain project-specific.
Workload
Compute for modelling, analytics and validated environments with lifecycle, support and evidence needs declared before architecture.
Workload
Compilation, CI, containers, virtualisation and developer services with concurrency, storage and licence behaviour understood.
Workload
Edge or central platforms for production analytics, image processing and industrial AI without absorbing deterministic control authority.
Workload
MATLAB, Python, R, Julia and specialist numerical workloads sized from algorithms, datasets and parallel behaviour.
Workload review
The first scope can use “Not known”; secure transfer is arranged later for confidential or controlled material.