An explanation of clients, API tokens, and quotas in PaletteAI Inference Launchpad: what a client is, why the appliance serves many clients such as AI coding assistants, and how it meters usage, reports utilization, and limits consumption. It also covers how workspaces, roles, and members decide who administers which clients.
An explanation of estimated savings in PaletteAI Inference Launchpad: how the appliance compares serving on the appliance with a frontier model, and where to find it.
How external inferencing works on a PaletteAI Inference Launchpad appliance, including where an endpoint is set up, which key the appliance sends, the levers that keep external traffic occasional, and the most common misconfigurations.
An explanation of how PaletteAI Inference Launchpad lets two or more models share a GPU, how the console judges each model's GPU memory share, and why one appliance runs one sharing group.
How the PaletteAI Inference Launchpad appliance installs across two stages, the roles of the jumpbox, slim ISO, and Local UI, and why the network uses a bond rather than a bridge.
What it means for a model to be certified on PaletteAI Inference Launchpad, how models are certified, and how to choose the right models for your use case.
An explanation of how PaletteAI Inference Launchpad places a model on cluster nodes, when to pin a model to a subset of nodes, and how the Cluster view reports that placement.
An explanation of the PaletteAI Inference Launchpad architecture, including its component stack, data flow, network topology, and data residency model.
Background, context, and design rationale for understanding how PaletteAI Inference Launchpad works.
An explanation of inference engines in PaletteAI Inference Launchpad, including automatic engine selection, the supported engine kinds, and engine arguments.
An explanation of model recipes in PaletteAI Inference Launchpad: what a recipe contains, why recipes exist, how one recipe reaches the appliance in two uploads, and why the model is not in the catalog between them.
An explanation of how PaletteAI Inference Launchpad decides which model answers each request: the Tier map, the semantic router, where routing rules live, and how the semantic router shares the GPUs.
An explanation of the guard that the semantic router applies to each routed request on a PaletteAI Inference Launchpad appliance: its two checks, which requests each check applies to, and what happens when a check refuses one.
How the Thinking directive on the Tier Map controls reasoning depth by effort level, why the off option is disabled for some providers, how each engine interprets the level, and the shared output allowance that reasoning and answer tokens draw from.
An explanation of usage pricing in PaletteAI Inference Launchpad: what estimated cost is, the four per-model rates, and how cache reads, cache writes, and response cache hits are priced.
An explanation of vision preprocessing in PaletteAI Inference Launchpad: how a text-only model answers questions about images, what clients see, and the limits that apply.
PaletteAI Inference Launchpad is a standalone, turnkey AI appliance that lets enterprises run large language models on their own hardware without cloud dependency, AI consulting, or complex infrastructure setup.