Overview of the Model hub

The Model Hub is the central location in your GLBNXT Platform environment where AI models are managed, accessed, and monitored. It provides your development team with a unified view of every model available in your environment, along with the endpoints, configuration details, and usage information needed to integrate those models into your applications.

Every model available through the Model Hub is served through the platform's managed inference layer. GLBNXT handles model deployment, serving runtime configuration, and endpoint management. Your team connects to a stable, authenticated endpoint and the platform takes care of everything behind it.

What the Model Hub Contains

The Model Hub gives your team visibility into the full set of models available in your environment. For each model, the Hub provides:

• The model name, version, and a brief description of its capabilities and intended use cases

• The API endpoint URL used to call the model from your applications or workflows

• The serving runtime in use, either Ollama for open-source models or NVIDIA NIM for optimised production inference

• Current availability status and any active usage or performance alerts

• Usage metrics including request volume, average latency, and token consumption over time

Model Categories

Models in the Hub are organised by category to help your team identify the right model for a given use case.

Language models handle text generation, reasoning, summarisation, classification, question answering, and conversational tasks. These are the core models most AI applications on the platform are built around.

Embedding models convert text into numerical vector representations used for semantic search and retrieval-augmented generation. The embedding model used during document ingestion must match the model used at retrieval time.

Specialised models cover specific capabilities such as code generation, structured data extraction, or domain-specific reasoning. Availability depends on your environment configuration.

Accessing Model Endpoints

Model endpoints are listed in the Model Hub with the full endpoint URL and authentication requirements. All endpoints require authentication using credentials managed through the platform secrets vault. Your application references the credential by name and the platform injects the actual value at runtime.

Endpoints follow a consistent API format compatible with standard AI development frameworks, meaning that applications built against GLBNXT-hosted model endpoints can be adapted with minimal changes if model selection changes over time.

Adding and Updating Models

The models available in your environment are configured during onboarding based on your use case requirements. If your team needs access to additional models or a newer version of an existing model, this is handled through a request to your GLBNXT contact. GLBNXT validates, deploys, and tests the model before it appears in your Model Hub.

Custom or fine-tuned models developed by your team can also be deployed into the Model Hub. Contact your GLBNXT contact to discuss the process for bringing a custom model into your environment.

Model Transparency

Every model in the Model Hub is clearly attributed. Your team can see which model is serving a given endpoint, which version is deployed, and what the model's training origin is. GLBNXT does not use your data to train or fine-tune models. Outputs generated by models in the Hub are never used to update or modify the underlying model weights.

For further guidance on model serving and routing, see the Model Serving and Routing section. For guidance on embedding models and their role in RAG pipelines, see the RAG and Knowledge Systems section.