Quick guidance on choosing self-hosted AI models (LLM/VLM/Embedding) based on task, VRAM budget, cost, and quality requirements, including quantization level and VRAM sizing. Use when picking a model for a new task, checking what fits a VRAM limit, or comparing two models. Not for tasks already committed to a model or using a hosted API (OpenAI/Anthropic).
How to install ai-model-selector?
hotak92/vibecoded-orchestrator/ai-model-selector
$npx -y skills add hotak92/vibecoded-orchestrator --skill ai-model-selectorInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/hotak92/vibecoded-orchestrator" --skill "hotak92/vibecoded-orchestrator/ai-model-selector"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/hotak92/vibecoded-orchestrator" that are relevant to the current task. Run `npx skills add "https://github.com/hotak92/vibecoded-orchestrator"` and select the relevant skills, then follow their instructions.
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