+Developers

On-Box Models & GPU Control

Keep an LLM library on-box. Build custom models, allocate GPUs, isolate workloads, and trace compute activity.

YOUR MODELS. INSIDE YOUR ENVIRONMENT.

Keep an LLM library on-box. Build models of your own.

Stage approved models in a local LLM library, build custom LLMs for your application, and run them on local GPUs. Keep model weights on the inference hosts and training datasets in the governed data foundation.

On-box LLM library

Manage approved model versions, configuration, and availability locally. Pre-stage weights and dependencies so runtime inference can work without an internet connection.

Build custom LLMs to run on-box

Prepare authorized datasets, train or fine-tune models sized for available compute, evaluate their behavior, and version the approved result for local deployment.

Local inference + AI agents

Make approved models available to your separate application and on-box AI agents through scoped API or MCP access, with policies and logged model activity.

REFERENCE ARCHITECTUREILLUSTRATIVE

Authorized training datasetsPrepared through your private ETL / ELT pipelines

Custom LLM builderTrain / fine-tune → evaluate → approve → version

Publish approved model locally

On-box LLM libraryApproved base models + your custom LLMs

Local GPU inferenceYour application + on-box AI agents via APIs / MCP

Weights and runtime dependencies staged on-box

Configure isolation, replication, recovery, and GPU capabilities for the selected infrastructure. Validate each deployment against your application's operating requirements.