Electricity Cost of Running a Local LLM 24/7

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 10 lipca 2026

Running a local LLM 24/7 costs between $1 and $30 a month in electricity depending almost entirely on your hardware: an always-on Mac Mini costs less than a nightlight, while a dual-GPU tower approach

In This Guide

• The Formula (30 Seconds of Math)
• Local AI Power Consumption by Hardware Tier
• What a 24/7 LLM Server Costs per Month
• The Two Settings That Cut the Bill Most
• Measure, Don't Guess
• PSU Sizing for an AI Box
• Frequently Asked Questions
• Related Guides

Running a local LLM 24/7 costs between $1 and $30 a month in electricity depending almost entirely on your hardware: an always-on Mac Mini costs less than a nightlight, while a dual-GPU tower approaches a streaming-service budget. This guide gives real idle and load wattage by GPU tier, a formula to fill in with your own rate, and the two settings that cut the bill the most.

Last Updated: July 2026

The Formula (30 Seconds of Math)

Monthly cost = watts ÷ 1000 × hours per day × 30 × your rate per kWh

Example: a PC that idles at 80W around the clock, at the 2026 US average of ~$0.17/kWh: 80 ÷ 1000 × 24 × 30 × 0.17 = $9.79/month — before the GPU does any actual work.

Your electricity rate matters more than any hardware choice within a tier: the US average is ~$0.17/kWh, Germany is roughly double, and time-of-use plans can swing 2x between midday and overnight. Check your bill, not a national average.

Local AI Power Consumption by Hardware Tier

Measured-at-the-wall figures (whole system, not GPU alone):

The number people get wrong: idle. A personal LLM server spends 95%+ of the day idle — the model sits in memory waiting. A gaming tower's 70W idle costs ~$8.60/month at $0.17/kWh doing *nothing*, while a Mac Mini's 5W idle costs $0.61. Inference bursts barely register by comparison: even two full hours of generation per day on a 4090 at 450W adds only ~$4.60/month.

What a 24/7 LLM Server Costs per Month

Assuming a realistic personal-assistant duty cycle (22h idle + 2h active inference per day):

Run the numbers for your own model and GPU in the cost calculator. Two honest conclusions: for always-on duty, Apple Silicon or a mini PC is dramatically cheaper than any tower; and even the "expensive" 4090 box costs less per month than a single ChatGPT Plus seat — the local vs cloud analysis has the full break-even math including hardware amortization.

The Two Settings That Cut the Bill Most

1. Power-limit the GPU (biggest win, near-zero downside)

LLM inference is memory-bandwidth-bound, not compute-bound — the GPU's top power band is mostly wasted. Capping an RTX 4090 from 450W to 300W typically costs only ~5–10% tokens/sec; a 3090 capped from 350W to 250W behaves the same:

# Cap the GPU at 300W (resets at reboot; persist via a startup script)
sudo nvidia-smi -pl 300

# Verify under load
nvidia-smi --query-gpu=power.draw,power.limit --format=csv -l 5

That single command saves a heavy 24/7 user $3–8/month and cuts heat and fan noise proportionally.

2. Don't keep models loaded on power-hungry cards

Ollama unloads models after 5 minutes by default (OLLAMA_KEEP_ALIVE), letting the GPU drop into deep idle. If you set OLLAMA_KEEP_ALIVE=-1 for instant responses, know the price: a 3090 holding a model idles 20–30W higher than an empty one. Keep models resident on efficient hardware; let them unload on big NVIDIA cards.

Also worth checking on headless Linux servers: if nvidia-smi shows 40W+ at idle with nothing loaded, the card is stuck in a high P-state — look up persistence mode and P-state fixes for your driver version.

Measure, Don't Guess

• Whole system: a $15–25 wall meter (Kill A Watt style) is the only number that includes PSU losses, fans, and drives.
• GPU alone: nvidia-smi --query-gpu=power.draw --format=csv -l 1 — accurate for trends, though it misses millisecond transients.

PSU Sizing for an AI Box

Modern GPUs draw sharp millisecond spikes far above rated power — a 4090 can transiently pull 600W+. An undersized PSU produces the classic "PC reboots the moment the model loads" failure:

• RTX 3060/4060 class: 550–650W quality unit
• Single 3090/4090: 850W (1000W for spike-prone early 3090s)
• Dual GPU: 1200W+, ideally ATX 3.x with native 12V-2x6 connectors

An 80+ Gold or Platinum unit also wastes fewer watts as heat — at 24/7 duty, the efficiency premium repays itself over the PSU's lifetime.

Frequently Asked Questions

How much electricity does a local LLM use per query?

Almost nothing. A 4090 generating a 500-token answer for 15 seconds at 450W uses about 1.9 watt-hours — three hundredths of a cent. Per-query cost is a rounding error; *idle hours* are the entire bill for a 24/7 machine.

Is it expensive to leave Ollama running 24/7?

On a Mac or mini PC, no — $1–3/month all-in. On a gaming tower with a big GPU, the tower's own idle draw costs $8–17/month depending on your rate. If the machine is mostly a server, efficient hardware saves more than any software tweak.

Does running a local LLM heat up the room?

Every watt becomes heat. A 450W inference load is a space heater on low; in summer with air conditioning you pay for those watts twice — once to make the heat, once to remove it. Power-limiting the GPU helps, and so does free ventilation.

Is local AI still cheaper than cloud APIs once electricity is counted?

For steady personal use, usually yes: even a 4090 box's ~$10/month electricity undercuts equivalent API spend for heavy users, and the hardware amortizes over years. The full comparison — hardware cost included — is in the Local AI vs Cloud cost guide.

Related Guides

• → Local AI vs Cloud: Cost Analysis
• → Mini PC for Local AI
• → Buying a Used GPU for Local AI
• → Personal AI Home Server

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