06Capability
Research compute
& AI.
Accelerated compute for research and AI workloads, engineered with the same discipline as the rest of the estate: observable, recoverable, and honest about its constraints.
GPUs are
infrastructure too.
Accelerated compute tends to arrive as an exception: hand-built hosts, snowflake drivers, workloads nobody can restart, and utilization nobody measures. The hardware is expensive precisely where the engineering is thinnest.
NetFRAME treats GPU systems as part of the machine: provisioned from code, scheduled deliberately, monitored at the device level, and integrated with the storage and network paths their workloads actually stress.
01
GPU infrastructure
NVIDIA GPU platforms integrated into virtualized and bare-metal compute: driver and runtime lifecycles managed as code, device passthrough and isolation engineered on purpose, and capacity understood rather than guessed.
Distributed and containerized research workloads run on infrastructure that can be rebuilt, so a failed node is an interruption rather than a loss of work that lived nowhere else.
02
Inference & model serving
AI inference infrastructure and model-serving concepts applied with production discipline: versioned models, controlled rollout, resource isolation, and behavior under load that has been measured rather than assumed.
The serving layer is engineered against the same questions as any service: what does it depend on, how does it degrade, and how is it observed?
03
Telemetry & data movement
Telemetry for accelerated compute at the level that matters: device utilization, memory pressure, thermals, and power, feeding the same observability stack as the rest of the estate.
Data movement treated as part of the workload: staging, scratch, and result paths designed against the storage and network fabric, because a starved GPU is a scheduling and I/O problem wearing a hardware costume. This work connects directly to infrastructure intelligence: the Jarvis program applies AI-assisted analysis to operations itself.
Discipline scope
- NVIDIA computeGPU platforms with managed driver and runtime lifecycles.
- Research workloadsDistributed, containerized compute on rebuildable hosts.
- Inference servingModel serving with versioning, isolation, and measured load behavior.
- GPU telemetryDevice-level metrics in the estate-wide observability stack.
- Data movementStaging and scratch engineered against storage and fabric.
- AI-assisted operationsOperational analysis as an applied R&D direction (Jarvis).
Related