Cloud Natives
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Solutions

Infrastructure for workloads that refuse to wait.

Seven practices, one engineering team. We design, build, benchmark and operate the compute layer beneath your hardest problems — on-premise, hybrid or sovereign cloud.

All solutions
AI & AI Factories Enterprise AI infrastructure, on-premise and hybrid LLM deployment, sovereign data governance, token pipeline optimisation. High Performance Computing Scalable compute clusters, R&D workload orchestration, advanced simulation and high-throughput parallel computing. Data Management & Storage Ultra-low-latency architectures, high-IOPS storage tiers and scalable multi-petabyte hybrid data pipelines. High-Frequency Trading Systems Ultra-low latency, mission-critical custom architectures and performance-tuned infrastructure down to the nanosecond. Quantum Computing Hybrid quantum-classical frameworks and exploratory development environments for teams starting the transition. Servers & Workstations Specialised compute, GPU-dense server racks and AI/ML data science workstations, built and burned in to spec. Managed Services 24/7 infrastructure monitoring, turnkey solution deployment, cloud optimisation and ongoing consulting. Start with a workload assessment Send us the job, the dataset shape and the deadline. You get a profiled bottleneck analysis and an honest answer about fit — before anything is quoted.
Not sure where your workload fits? Our engineers will benchmark it before you commit. Schedule a discovery call

Industries

Bespoke architecture, sector by sector.

A genomics pipeline and a trading engine are not the same machine. We start from your regulatory perimeter, your data gravity and your deadlines — then design backwards.

All industries
Government & Public Sector Data sovereignty, ISM controls evidenced through IRAP assessment, and resilient citizen service infrastructure. Defence & National Security Ruggedised mission-critical compute, strict sovereign compliance and secure air-gapped environments. Health Tech & Life Sciences Accelerating genomic sequencing, diagnostic AI workloads and medical imaging pipelines. eResearch & Academia Scalable HPC clusters enabling scientific discovery, climate modelling and multi-university collaboration. Media & Entertainment GPU rendering pipelines, VFX workflows and high-throughput media storage that keeps artists working. Startups & Scale-ups Right-sized, growth-oriented infrastructure that scales from proof-of-concept to production without a rebuild. The Arts & Culture Large-scale digital archiving, GPU-accelerated interactive media and creative technology platforms. Deployment stories from your sector How the constraint was identified, what the architecture looked like, and which numbers moved. Written by the engineers who built it.
Every engagement begins with a workload assessment, not a product catalogue. Read deployment stories

Solutions

Seven practices. One engineering team. Nothing quoted before it is measured.

Cloud Natives designs, builds and operates compute infrastructure for Australian organisations that have run out of headroom. We start with your job on our bench, not with a reference architecture. If the measurement says you do not need what you came to buy, that is the advice you get.

Capability

Pick the practice that owns your bottleneck.

The seven practices share one team, one set of instrumentation and one review board. Most engagements touch three of them, because a training run that misses its window is rarely a GPU problem alone.

AI & AI Factories

An AI factory is a power and data-path problem wearing a GPU costume. We size the accelerator fabric to the model rather than the budget line, keep weights, prompts and embeddings inside your own perimeter, and publish a cost per million tokens you can put in front of a finance committee. Training and inference get designed separately, because they fail for different reasons.

  • NVLink domain sizing, RDMA east-west fabric and GPUDirect Storage paths
  • Private inference on vLLM or TensorRT-LLM behind your identity provider
  • Retrieval and fine-tuning pipelines with lineage an auditor can follow
  • Rack power, cooling and density modelling before any hardware is ordered

Design constraint — tokens per kilowatt

High Performance Computing

Clusters are judged on time-to-result, not peak FLOP/s. We profile your solvers, choose an interconnect that matches their communication pattern, then tune the scheduler until the queue stops being the slowest component in the building.

  • Slurm partitioning, fair-share weighting and QoS design
  • InfiniBand or high-speed Ethernet topology sized to MPI collectives
  • OpenFOAM, GROMACS, LS-DYNA and WRF benchmarked on your own meshes
  • Direct-to-chip liquid cooling for dense CPU and GPU nodes

Design constraint — the research window

Data Management & Storage

Accelerators starve long before they saturate. We build parallel file systems and NVMe-oF fabrics that hold throughput at the 99th percentile, then prove it under the concurrency your estate actually generates rather than a single-stream test.

  • Lustre, BeeGFS and WEKA deployments sized to concurrent job count
  • NVMe-oF and RDMA data paths with measured p99 read latency
  • Policy tiering from flash to tape, with recall actually rehearsed
  • Immutable snapshots and offline copies for ransomware recovery

Design constraint — sustained p99, not peak

High-Frequency Trading Systems

A latency claim you cannot reproduce is a marketing number. We co-design host, NIC and switch, remove the jitter sources one at a time, and hand over hardware-timestamped histograms for every change so the desk can audit the gain.

  • Kernel bypass, core pinning, NUMA placement and IRQ layout
  • FPGA and host co-design for feed handlers and pre-trade risk gates
  • PTP and PPS distribution from GNSS-backed grandmaster clocks
  • Colocation cage, cross-connect and exchange onboarding

Design constraint — determinism at p99.9

Quantum Computing

Useful quantum advantage has not arrived, and we will put that in writing. What is worth building now is the classical scaffolding around it: simulation capacity, orchestration, and a cryptographic inventory so the eventual transition is an upgrade rather than a rebuild.

  • State-vector and tensor-network simulation on your existing HPC nodes
  • Qiskit and PennyLane environments with brokered access to third-party QPUs
  • Hybrid workflow orchestration alongside Slurm and Kubernetes
  • Post-quantum cryptography inventory and migration sequencing

Design constraint — honesty about the timeline

Servers & Workstations

Specified against a measured workload profile, assembled in Australia, and soak tested before it leaves the floor. The benchmark report arrives with the invoice, so you can see what you bought rather than which catalogue tier it came from.

  • Bill of materials tied to a profiled job, not a product family
  • GPU-dense chassis with power, airflow and acoustic modelling
  • Driver, CUDA and container stacks pinned and validated per machine
  • Burn-in, thermal soak and on-site commissioning against acceptance tests

Design constraint — what the workload profile says

Managed Services

The build is the short part. We operate the estate afterwards from an in-country operations centre, review capacity and spend every month, and make sure the engineer who answers at 3 a.m. already knows your topology.

  • Monitoring, on-call rotation and documented escalation paths
  • Kubernetes and Slurm platform operations under change control
  • Commitment strategy, rightsizing and egress review each quarter
  • Architecture review against your roadmap, on a retained basis

Design constraint — who carries the pager

First Constraint

The bottleneck is almost never the accelerator.

Five things decide whether a compute estate performs, and silicon selection is the last of them. We measure all five before recommending anything, because getting the order wrong is how organisations buy hardware they cannot power.

01

Power and thermal envelope

A rack you cannot cool is a rack you cannot use. We model kilowatts per rack, return-water temperature and containment strategy before choosing silicon, because the building usually decides the architecture. Where the facility cannot take the density, we say so at week one rather than at commissioning.

02

Memory bandwidth

Most jobs described as compute-bound are bandwidth-bound. A roofline analysis tells us whether more cores will help or whether the answer is high-bandwidth memory and a different node shape. It takes about a day to find out and routinely changes the bill of materials by six figures.

03

The storage path

Between the media and the application there are a dozen places to lose a gigabyte per second: the file system client, the page cache, the protocol, the switch buffer, the queue depth. We instrument each hop instead of trusting an aggregate figure from a data sheet.

04

Queue and scheduling policy

High utilisation means nothing if the jobs that matter wait three days. Fair-share weighting, preemption rules and reservation policy are governance decisions with technical consequences, so they belong in the architecture document rather than in an operations ticket six months later.

05

Fabric topology

Collective operations expose topology mistakes that point-to-point tests never will. We test with the collectives your code issues, at the message sizes it issues them, then size oversubscription accordingly. Only after that does the choice of accelerator become an interesting question.

How We Engage

Five gates, and you can stop at any of them.

Each gate produces an artefact you keep, whether or not you continue. There is no discovery phase that exists only to justify the next phase.

  1. Workload assessment

    We take the real job: the mesh, the model checkpoint, the market data capture, the sequencing run. Then we profile where the wall-clock time actually goes. No questionnaire, no maturity matrix, no capability heat map. Typically one to two weeks, depending on how easily the workload can be shared.

  2. Benchmark on our bench

    The workload runs on representative hardware in our lab and you get the raw numbers, including the runs that disappointed. If a cheaper configuration wins, we will tell you, and the engagement can reasonably end there. We would rather lose the order than defend a benchmark we did not believe.

  3. Architecture and costing

    You receive a topology, a bill of materials, a power and cooling budget and a three-year total cost of ownership, with every assumption listed so your team can argue with it. Where a public-cloud option is genuinely cheaper for your duty cycle, the model shows that too.

  4. Build, burn-in, commission

    Systems are assembled and soak-tested in-country, then commissioned on site against the benchmark from gate two. Acceptance is a measurement with a pass threshold, not a signature on a delivery docket. Anything that fails soak testing does not ship.

  5. Operate and review

    Monitoring, on-call and change control from an Australian operations centre. Every quarter we review capacity, spend and incidents, and state plainly what we would design differently with what we now know about your workload.

Delivery Models

On-premise, hybrid, or sovereign cloud.

This is a per-workload decision, not a per-organisation one. Most estates we build end up mixed: steady-state training on owned hardware, seasonal peaks in a sovereign region, and one air-gapped enclave that never touches either.

Indicative characteristics only. Figures require verification before publication.
Decision factor On-premise Hybrid Sovereign cloud
Data residency Your facility, your jurisdiction. Physical custody is demonstrable to an assessor on a site visit. Split by classification. Requires a documented data-flow map and enforced egress controls. Australian regions with Australian-operated support. Contractual and physical residency, no offshore administration path.
Cost shape Capital up front, low marginal cost per job. Wins where sustained utilisation stays above roughly 60 per cent. Capital for the steady state, operating spend for the peaks. Needs a commitment strategy or you pay twice. Operating spend only. Predictable monthly, and more expensive per FLOP once load is continuous.
Time to first workload Roughly 8 to 16 weeks, gated by power, floor space and component lead times. Two to six weeks for the cloud portion, with the owned portion following. Days to weeks, subject to accreditation paperwork rather than engineering.
Elasticity Fixed. Burst capacity must be planned and funded a financial year ahead. Elastic at the edges, fixed at the core. The usual answer for research and rendering peaks. Elastic within regional capacity. Accelerator availability, not contract terms, is the practical ceiling.
Classification ceiling Up to and including fully air-gapped environments with no external route. PROTECTED where the boundary is documented, tested and enforced. PROTECTED under assessed control sets. Confirm the current assessment status in writing.
Key custody You hold the hardware security module and the keys. We never need them. Split custody with a documented break-glass procedure and dual control. Customer-managed keys, held in-country, revocable by you without a support ticket.
Typical fit Sustained model training, defence enclaves, sub-microsecond trading paths. Research with seasonal demand, media rendering, platforms growing faster than their forecast. Agencies without a data-centre programme, regulated startups, disaster recovery estates.
Talk through which model fits How we operate what we build

The Bench

We benchmark before we quote, and we show the failures.

The bench is a small lab with representative silicon across the practices, kept deliberately heterogeneous so comparisons mean something. Your workload runs on more than one configuration and you see all the results, including the ones that argue against the more expensive option.

  • Current and prior-generation GPU nodes, air and liquid cooled
  • InfiniBand and 400 GbE fabric segments for collective testing
  • Parallel file system test rigs on NVMe with tape recall behind them
  • Hardware timestamping and a GNSS-locked grandmaster for latency work
Send us a workload
Facility photography — benchmark lab, instrumented rack. 1600×1200
2 wks Median time from workload received to benchmark report
1 in 5 Assessments that conclude no new hardware is required
3 Configurations tested per workload, minimum

Procurement

The questions contracting teams ask first.

Answers written for the person who has to defend the purchase, not the person who wants the hardware.

Continue

Two useful directions from here

Gate One

Tell us which of the five constraints you have already ruled out. We will measure the rest.

A workload assessment is a fixed-price piece of engineering with a written report at the end. You keep the data and the harness whatever you decide to do next.

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Capability statement and referee list available under a mutual non-disclosure agreement.