Lizard inference engineering: Onboarding should recommend only compatible models
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Inference Engineering · Day 25 · Morning
Onboarding gets more useful when it stops treating every model as a possible choice.
The practical step is simple: check the model metadata, the quantization target, the machine’s detected hardware, and the estimated memory footprint before the user sees a recommendation. Lizard does that up front, then presents a native choice only when the model fits the current system.
That matters because a bad recommendation is not a minor UX flaw. It sends people into dead ends: wrong quantization for the available backend, a model that overshoots memory, or a file that looks plausible but will never run well on the detected hardware. If the flow knows the machine cannot carry the load, it should say so plainly and keep the rest of the list out of the way.
The useful part is not just rejection. It is a recommendation with context: why this model fits, what constraint made it pass, and what changed if the user switches hardware or memory budget. That gives the onboarding flow something closer to an engineering check than a browse-and-hope catalog.
In practice, this also keeps the local model path honest. A recommendation should reflect what the runtime can actually execute, not what a registry happens to offer.
What one signal do you trust most when you decide whether a local model is safe to recommend before download?
Engineering fact: Lizard combines model metadata, quantization compatibility, hardware capability, and memory estimates before presenting a native recommendation.
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#localai #modelonboarding #modelselection #windowsai #llmops
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