Local AI Inference Cost Calculator

v20261022

cost-local

Calculate the cost per million tokens for running AI models locally on your own hardware like Ollama or llama.cpp. It factors in electricity prices, hardware costs, power consumption, and generation speed to determine the break-even point against hosted API models. Useful for deciding whether to deploy models locally or use cloud services based on actual usage and financial constraints.

AI Cost Local Inference Hardware Decision LLM Optimization

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Overview

Cost Local

node ${CLAUDE_PLUGIN_ROOT}/scripts/local-cost.mjs --tok-per-s 80 --busy 0.25 --compare claude-haiku-4-5

Inputs (all the user's): --watts-idle, --watts-active, --kwh, --hw-price, --life-years, --resale. --tok-per-s is required and must be a measured generation speed.

Rules

Cost = fixed (amortisation + idle power, paid busy or not) + marginal (extra watts while generating). Utilisation decides the answer: always show the table at 5/25/50/100% busy.
It is an estimate from the inputs; say so. Nothing is measured.
Compare against a same-capability hosted model, not a frontier one; price alone is a poor reason to buy a GPU for sporadic use.
Read real counts from the server (Ollama eval_count/prompt_eval_count on the native API, llama.cpp timings, vLLM /metrics), not the lossy OpenAI-compatible usage, and count model reloads (load_duration) as overhead.

Info

Category Artificial Intelligence

Name cost-local

Version v20261022

Size 1.31KB

Source ruvnet/ruflo

Updated At 2026-10-23