Infrastructure & Compute

We run our own AI infrastructure.

Paladyn builds and operates self-hosted Linux AI infrastructure. Running it day to day is how we learn what private AI deployments actually require.

Fig. 05 — The layers of a private AI system. Illustrative; not a map of Paladyn's own environment.

The stack

Every layer, operated hands-on

Observability

Metrics, alerts and health checks across the whole stack.

Serving

Local inference and batch jobs scheduled against available accelerators.

Containers

Isolated, versioned services that can be rebuilt from configuration.

Operating system

Linux, drivers and GPU runtimes kept consistent and reproducible.

Hardware

Multi-GPU systems, storage, cooling and power planning.

Capabilities

C.01

Building, configuring and scheduling work across several accelerators in one machine.

C.02

Serving models on owned hardware with predictable latency and no data leaving the premises.

C.03

Rootless containers that keep services separated, versioned and easy to rebuild.

C.04

Metrics and dashboards for GPUs, services and storage, with alerts before problems become outages.

C.05

Steady and sensitive workloads run locally; bursty work can use cloud capacity through explicit boundaries.

C.06

Scheduled, verified backups of configuration and data, so systems can be restored rather than rebuilt from memory.

Hybrid compute

Local where it matters. Cloud where it helps. The boundary between them is designed, not left to defaults.

For security, Paladyn does not publish details of its internal network, hardware inventory or configuration.

Private AI