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localtune

Benchmark local LLMs on your own hardware, and find out which model, quantization and KV cache setting runs best on your machine.

Each run measures:

Metric Meaning
Prefill tok/s How fast the model reads your prompt
Decode tok/s How fast it writes the answer
TTFT ms Time to first token: the wait before the answer starts
Peak VRAM GB GPU memory used at the busiest moment
Avg / peak W GPU power draw
Energy Wh Electricity the GPU used during the run
Cost That energy times an electricity price

Data sources

Electricity cost uses the most local price localtune has: your zip code, then your state or country, then the US average. A price per kWh that you give yourself always replaces these. Each cost records which price it was based on.

Price Source Covers License
US zip codes NREL, U.S. Electric Utility Companies and Rates (2023), scaled by each state's change since 2023 (EIA) Zip codes where one company both sells and delivers power CC BY 4.0
US states EIA-861M monthly sales and revenue by state, last 12 months All 50 states and DC Public domain
Countries Eurostat, electricity prices for household consumers (nrg_pc_204), all taxes included, in euros 41 European countries CC BY 4.0
US average EIA Short-Term Energy Outlook, October 2026 Whole US Public domain

The tables in src/localtune/data/ are built from these sources by scripts/build_price_tables.py.

License

Apache 2.0

Metadata

Release files for localtune 0.1.0

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Source distribution for localtune 0.1.0
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