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DI: Digital Immigrant

1 Oct, 11:10

Open in Telegram Share Report

Sunk Cost - how many years until local AI hardware pays for itself

The case for running models locally is that tokens stop costing money once the machine is bought. This calculator puts a number on the word "once".

- the default scenario is unkind: a $3,499 Mac Studio running Qwen3 27B at a moderate coding-assistant load saves $0.22 a day, which works out to 43.6 years before break-even
- every assumption is editable - electricity price, tokens per day, context window, generation speed, or simply the monthly API bill being replaced
- the key-value cache math is shown alongside, so a context window that feels free turns out to cost gigabytes of memory and generation speed

The value here is the reframe rather than the verdict: it forces anyone arguing for local inference to state the workload they are assuming out loud, and 99 comments against 46 points on Show HN suggests that argument needed having.

https://sunkcost.ai/

📎 Read also:
→ Computable GPU Index - an open reference price for one GPU-hour
→ Larridin priced the AI-native engineer - $920 a month in tokens
→ PwC - where the $31.6tn AI buildout leaves compute prices
How long until local AI pays for itself? — Sunk Cost
Which open models fit your machine, how fast they run, and how many tokens it takes to pay back the hardware against an API. Honest enough to say never.

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