07Technology
AI / GPU
compute.
Accelerated compute integrated into the machine: GPU platforms provisioned as code, serving engineered like any production service, and telemetry down to the device.
Measured
acceleration.
GPU infrastructure is where estates most often abandon their own standards: hand-built hosts, unmanaged drivers, and utilization nobody can state. The hardware is too expensive for that.
NetFRAME runs accelerated compute inside the same discipline as everything else: lifecycle-managed platforms, deliberate scheduling and isolation, and behavior that has been measured under load.
01
GPU platforms
NVIDIA GPU platforms in virtualized and bare-metal roles, with driver and runtime lifecycles managed as code and device passthrough engineered deliberately. Research and inference workloads run on hosts that can be rebuilt from definition.
Cloudflare's edge platform serves this site and provides a working context for edge compute concepts alongside the estate's own infrastructure.
02
Serving & telemetry
Inference serving treated as a production service: versioned models, controlled rollout, resource isolation, and measured degradation under load. The serving layer answers the same engineering questions as any other service.
GPU-aware telemetry (utilization, memory, thermals, power) feeds the estate observability stack, and the same operational data becomes raw material for the Jarvis program's AI-assisted systems analysis.
Technologies we work with
- NVIDIA GPU platformsAccelerated compute with managed lifecycles.
- Inference servingVersioned, isolated, load-measured model serving.
- GPU telemetryDevice metrics in the estate observability stack.
- CloudflareEdge platform serving this site's own infrastructure.
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