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AI Data Centers / Compute / Reference designs

Reference designs.

Cluster designs that are sized, wired, and benchmarked before a single production job runs. Owners buy a validated blueprint, not a science experiment.

Validation before procurement, not after the invoice

A reference design is proven against a target workload before it is committed to concrete and copper.

01

Workload-first sizing

Designs start from the target model and dataset profile, then derive accelerator count, fabric, and storage from it. The build follows the workload rather than a generic rack template.

02

Bottleneck analysis

Compute, memory bandwidth, fabric bisection, and I/O are balanced so no single tier starves the others. The design is tuned until the accelerators, not the plumbing, are the limit.

03

Pre-production benchmarking

Candidate configurations are run against representative training and inference workloads before being committed. Results calibrate the design; we publish the method, not invented numbers.

04

Documented assumptions

Every design records the workload, ratios, and constraints it was validated against. An owner can see exactly when the blueprint still applies and when it does not.

Composable pods that scale by replication, not redesign

Designs are built from validated units so growth is repetition of a known-good block rather than a new engineering effort.

01

The pod as a unit

A pod bundles accelerators, host CPUs, fabric, and local storage into a validated, repeatable block. Scaling a cluster means adding pods, each already proven in isolation.

02

Spine sized for scale-out

The inter-pod spine is provisioned so adding pods does not create fabric hotspots. Bisection grows with the pod count rather than being fixed at the first build.

03

Power and cooling zones

Each pod maps to a power and cooling zone so a facility fault is contained to one block. Zones are the same boundary the scheduler uses for failure domains.

The design reaches down to power, cooling, and the hall

A cluster is inseparable from the building around it, so the reference design covers the EPC envelope, not just the racks.

01

Liquid-ready halls

Designs assume direct liquid cooling for accelerator density and specify the distribution and redundancy to support it. Air-only halls are not retrofitted into pretending to handle the load.

02

Redundant power topology

Power paths are dual and independent so a distribution fault degrades rather than drops the cluster. Feeds are sized for sustained training draw, not nameplate averages.

03

EPC integration

Reference designs align with hyperscale EPC delivery so the compute and the building are engineered together. The blueprint accounts for what the site can actually deliver.

04

In-nation siting

Designs assume facilities inside the owner's jurisdiction to satisfy data residency by construction. Siting is a design input, not a later compliance patch.

A blueprint an owner can audit and reproduce

Sovereign infrastructure must be inspectable, so the design itself is a governed, versioned artifact.

01

Versioned design artifacts

Topology, bill of materials, and configuration are captured as versioned artifacts the owner keeps. A design can be reproduced or diffed against what was actually built.

02

Signed provenance

Design and firmware artifacts are signed with post-quantum ML-DSA-65 so their authenticity survives a future quantum adversary. An owner can verify a build matches the approved design.

03

Ledger-anchored acceptance

Design validation and acceptance milestones are recorded on the tamper-evident hash-chained ledger. The record of what was proven cannot be silently rewritten later.

04

Owner-controlled evolution

Changes to a reference design go through review the owning institution governs. The blueprint evolves under the owner's authority, not the platform's.

Build it sovereign.

Talk to us about reference designs in a sovereign deployment.