There is no fixed monthly price for running a local large language model (LLM). The electricity cost depends on the computer’s power draw at the wall, how long it runs, and your electricity rate. If the computer would be on anyway, count only the extra draw caused by the LLM to estimate its incremental cost. If it is a dedicated machine left on all day, include its idle time too.
Calculate the monthly electricity cost
Use this formula for the part of the computer’s power use you want to account for:
Monthly cost = (average wall watts ÷ 1,000) × hours per month × electricity price per kWh.
Use whole-system wall power where possible: the GPU, CPU, memory, storage, power-supply losses, and other components all contribute. A GPU’s own power reading is not the same as the computer’s total draw at the outlet.
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For a computer you already use
Measure or estimate the system’s baseline draw while it is on but not doing LLM work. Subtract that baseline from the average draw during LLM use, then multiply the difference by the LLM-use hours. This estimates the workload’s incremental electricity cost rather than charging the LLM for electricity the computer would have used anyway.
For a dedicated, always-on computer
Include the machine’s idle draw during the hours it is waiting as well as its active draw during inference. If the system stays on continuously, using 720 hours represents a 30-day month. A machine that sleeps or shuts down for part of the day will use less.
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What the estimate looks like at a U.S. benchmark rate
The U.S. Energy Information Administration’s July 2026 residential-sector average is 18.31¢ per kWh. This is a national benchmark, not a personal tariff; the EIA’s July table also shows state variation, including 30.49¢ in Massachusetts and 32.41¢ in Maine. Check your own bill or utility tariff for a personal estimate, especially if your rate changes by time of use. EIA monthly electricity data is updated, so these prices can change.
| Average wall draw | Hours assumed | Monthly electricity cost at $0.1831/kWh |
|---|---|---|
| 100 W | 720 hours (30 days, continuously) | About $13.18 |
| 200 W | 720 hours (30 days, continuously) | About $26.37 |
| 500 W | 720 hours (30 days, continuously) | About $65.92 |
These are arithmetic scenarios based on the stated power levels and the EIA benchmark, not measurements of particular computers. A different rate, draw, or schedule changes the result proportionally. For example, a computer averaging 200 W for four hours a day uses far less than the same system left at 200 W around the clock.
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Why model and workload change power use
Local inference does not have one universal energy rate per response or token. Model architecture, quantization, reasoning behavior, context length, and the work being requested can all affect energy use. Comparing a small model’s draw with a larger model’s draw is not useful unless both are doing a comparable task at an acceptable quality level.
A preliminary benchmark posted June 12, 2026 by Philipp M. Zähl, Elja Dalipaj, Anika Hennig, and Timon Bayer tested 18 open-source models using Ollama on one NVIDIA RTX 4060 Ti 16GB. It sampled GPU draw with nvidia-smi at 2 Hz and reported 0.2747 joules per output token for Qwen 2.5 0.5B; the tested 7B Mistral result used up to 8.6 times more energy per token than the most efficient model in that test. Those are GPU-side observations from one setup, not whole-computer wall measurements or a reliable monthly-cost multiplier for other systems. Read the preliminary benchmark.
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Choose a setup by fit and intended use
The practical choice is not simply “the lowest-watt model.” You need a model that fits your hardware and handles your tasks at useful quality and speed. NVIDIA’s guide gives 6–8 GB, 12–16 GB, and 24 GB or more of RTX memory as example starting points for different model sizes. Quantization can reduce memory needs, but may affect response quality; longer context also uses more memory. These are selection considerations, not guarantees of a particular electricity cost. NVIDIA’s local LLM guide.
- Model and task: Compare systems on the same kind of request and a similar quality target.
- Memory and context: Check that the model and the context length you need fit comfortably in available GPU memory.
- Usage pattern: Distinguish short sessions from a model kept loaded to serve requests throughout the day.
- Compatibility: Ollama’s GPU support depends on hardware, operating system, and driver/runtime requirements. Confirm your exact combination in the current Ollama GPU documentation before choosing hardware.
Keep electricity separate from hardware cost
The formula estimates recurring electricity, not the cost of buying or replacing a computer. Keep hardware purchase cost separate when comparing local use with other options; for light workloads, the upfront purchase may matter more than the electricity. The available benchmark’s RTX 4060 Ti 16GB is a test component, not a recommendation or evidence that it is today’s best purchase.
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NVIDIA says local prompts, files, and context can remain on the user’s machine, and describes on-device use as having no usage limits or subscription fees. That describes service access; it does not make hardware or electricity free, and it does not establish the privacy behavior of every application or workflow. Check the software you use and how it handles data.
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