The three ways to actually run an open model: on your own hardware (local, $0 compute), through a hosted pay-per-token API, or by renting a cloud GPU and serving it yourself.
Running it locally (Ollama, LM Studio, llama.cpp) costs nothing per token beyond electricity, keeps data private, and works offline — but you're limited by your own VRAM and speed, so big models may not fit or may run slowly.
A hosted API charges per token (priced per million tokens), needs no hardware, and scales instantly; it's cheapest when your usage is light or spiky, but cost grows with volume and your data leaves your machine. Renting a cloud GPU (e.g. by the hour) and serving the model yourself with vLLM gives you big-model performance without buying hardware, and is often cheapest at sustained high volume — but you pay for the GPU whether it's busy or idle and you manage the setup.
This trade-off is exactly what Spanvero exists to make transparent: for any model it compares the real cost of local ($0 compute), bring-your-own-key API, and rent-your-own-GPU — with zero markup — so you can pick the genuinely cheapest route for your situation.
VRAM · vLLM · Ollama · Inference
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