High-Performance Computing

Sovereign HPC

Simulation and AI on one cluster, inside your own borders.

Sovereign HPC runs classical high-performance computing and cloud-native workloads on the same physical estate. Simulation codes submitted to a batch queue and containerised AI jobs orchestrated through Kubernetes draw on one pool of compute, one high-throughput storage tier and one low-latency fabric. It is deployed in your own data centres, on hardware you own, under your own identity and audit controls.

Many organisations end up owning two machines: a research cluster booked weeks ahead, and a container platform the application teams treat as always-on. Both sit idle at different hours, and capital is committed twice. Consolidating them recovers that headroom and lets an AI programme start on infrastructure the institution already has, rather than on a foreign cloud account.

Architecture

How it is put together

Workload submission

Researchers, engineers and data teams submit work through a web console, a command line or an API, with batch job scripts, containerised pipelines and training runs all entering through the same door.

Unified scheduling

A single scheduling layer places every job, arbitrating between batch queues and container workloads against policies for priority, data locality, accelerator type and energy.

Runtimes and environments

Reproducible container images, MPI runtimes and partitioned accelerators give each job a pinned, portable execution environment on x86 or ARM nodes.

Data and fabric

A parallel high-throughput file system holds working data, object storage holds datasets, checkpoints and results, and an RDMA interconnect carries traffic between nodes at low latency.

Sovereign infrastructure

Compute, storage and network sit in your own data centres and edge sites, under your identity, audit and change control, with no dependency on a foreign operator.

Capabilities

Batch and containers together

A batch queue and a Kubernetes cluster share the same nodes, so simulation jobs and containerised services draw on one pool of compute instead of two separate machines.

Elastic clusters on demand

Batch partitions are created and dissolved as programmes need them, giving a research group a private cluster for the length of a project without buying it dedicated hardware.

Cross-site scheduling

Placement policies weigh queue depth, data locality, accelerator type and site capacity to decide where a job runs across a central cluster and any connected secondary or edge sites.

Low-latency fabric and storage

Tightly coupled MPI jobs get an RDMA interconnect and a parallel high-throughput file system, with object storage behind it for datasets, checkpoints and results.

Reproducible job environments

Work is packaged as containers with pinned dependencies, built for both x86 and ARM, so a run can be repeated later on different hardware in the same environment.

Fine-grained GPU allocation

Accelerators are partitioned and shared between jobs rather than held whole for the length of a booking, so short inference and pre-processing steps do not lock out training work.

Unified observability

Metrics, logs and job accounting from both the batch and container sides land in one place, including energy drawn per job, so energy use can be attributed to a project and fed back into scheduling policy.

Sovereign operating model

Engagements include source code access, upstream-compatible open-source foundations and structured know-how transfer, so your own staff can run and extend the cluster without depending on us.

Where it fits

A research institute runs simulation queues on one cluster and AI teams on another; both sit idle for long stretches and neither can borrow the other's capacity.
An epidemiological modelling group needs thousands of scenario runs, and needs any one of them reproduced exactly months later for review.
A meteorological service must deliver its forecast runs inside a fixed daily window, whatever else has been queued on the machine.
An engineering team runs tightly coupled MPI simulations that need a low-latency interconnect, while the data-science team next door wants the same accelerators for training.
A national agriculture or grid-monitoring programme collects data at remote sites, pre-processes it at the edge, and offloads the heavy analysis to the central cluster.

What you end up with

A single high-performance estate the organisation controls end to end — hardware it owns, source code it can read and change, and the operating knowledge to run both — with simulation and AI running side by side inside its own borders.