PaletteAI Inference Launchpad Explanation

Explanatory and conceptual guides help you understand how and why PaletteAI Inference Launchpad works the way it does. They cover design decisions, component relationships, and trade-offs rather than walking you through tasks.

Contents

Topic	What you understand
Architecture Overview	The component stack, request routing, model provisioning lifecycle, network topology, and data residency model.
Model Placement	How a model is placed on cluster nodes, when to pin it to a subset, and how the Cluster view reports placement.
Vision Preprocessing	How a text-only model answers questions about images by converting them to text first.
Clients and Quotas	What a client is, how quotas meter usage, and how utilization, consumption, and historical windows are reported.
Routing Behavior	How the Tier map and the semantic router combine to pick the model that answers each request.
Frontier Providers	Where provider keys live, the credential patterns, the levers that keep frontier traffic occasional, and bursting.
Model Certification	What certified means, how models are certified, and how to choose models for your use case.
Inference Engines	What an inference engine is, automatic engine selection, the supported kinds, and when to override it.
Installation Architecture	How the appliance installs, why the network uses a bond, and how day-two upgrades stay in Local UI.
The Thinking Directive	How the Thinking directive on the Tier Map controls reasoning depth by effort level and how each engine reacts.