Self-Hosted GPU vs API for LLM Workloads

Two postures have hardened around the cost of AI, and most leaders have already picked one without registering it as a choice. The first says zero dollars per token. Own the silicon, run the weights locally, drive the marginal cost of a query to nothing. Apple’s M3 through M5 put a capable model on a machine that fits in a backpack, NVIDIA’s GB10 desktop box puts a small token factory under the desk, and the appeal is clean: no meter, no vendor, no bill that grows every time the team does its job. ...

Everyone reaches for the GPU. At 114 megabytes, the GPU might be the wrong answer. This is the companion bench to Visual RAG Beats the Vision Model, where a distilled image-retrieval corpus beat a 3-billion-parameter vision model on accuracy at 180 times the speed. That whole argument rests on one quiet assumption: that the retrieval underneath is cheap. Cheap to run, cheap to host, cheap in watts. This article is where I check that assumption instead of asserting it, because an assumption you have not measured is just a hope wearing a lab coat. ...

292x. That’s not a rounding error. That’s the cost multiplier between running a batch inference job on a rented B200 GPU and sending the same workload through Claude Opus 4.6’s API. The job was straightforward: generate one or two contextual sentences for each of a million documents, extracted JSON from the corporate PDF archive I’ve been building a RAG pipeline around. Those sentences get prepended to each chunk before embedding into Qdrant’s 768-dimensional vectors with BM25 sparse indexing. It’s the contextual layer that makes retrieval actually work, the step I described in the previous article about why a million PDFs won’t organize themselves. ...