Articles

Two months of hands-on experiments on the real cost of running your own AI, from a laptop GPU to Google Cloud. The takeaways, the numbers, and when private AI actually makes sense.

A 24GB datacenter GPU running the same model, the same settings, was 4-5x less energy-efficient per token than a 4GB laptop card. Here's what that means for hardware selection.

What OpenAI, Anthropic, and Google's current API terms actually promise about your data — and how that compares to running the model yourself, on rented or owned hardware.

One run of an LLM inference benchmark showed an 8.3x slowdown. The exact same test, rerun unmodified, showed 5-6x — and the dramatic result didn't happen at all the second time.

We assumed our GPU was nearly maxed out at 96% utilization. Raising concurrency from 1 to 8 requests cut cost per token 4.8× anyway — with power draw barely moving.

Llama 3.1 8B and Qwen3-8B show the same concurrency-vs-cost curve on the same GPU — and what that does and doesn't tell you about sizing private LLM infrastructure.

Real cost and operational data from six infrastructure experiments — what self-hosting an LLM actually takes, versus what it looks like on a spec sheet.

Six experiments in private LLM inference — and four times the obvious metric pointed to the wrong decision.
Testing a Claude UI skill against a dedicated UX and accessibility audit — where visual polish ends and interaction correctness begins.