There is no single monthly price for running a local large language model (LLM). The electricity cost depends on the computer’s whole-system power draw, how long it runs, and the rate on your electricity bill. A GPU’s reported wattage is not the same as the power drawn by the entire PC—and if a dedicated machine stays on all day, its idle hours count too.
How to estimate a local LLM’s monthly electricity cost
Use this formula for the power draw attributable to the workload you want to cost:
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Monthly electricity cost = (average wall watts ÷ 1,000) × hours per month × electricity price per kWh
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“Wall watts” means power measured for the whole computer at the outlet, including the GPU, CPU, memory, storage, and power-supply losses. Use the marginal energy rate on your bill, including a time-of-use rate if it applies. The estimate covers electricity, not hardware purchase or replacement.
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If the computer would be on anyway
Measure or estimate the computer’s baseline draw without the LLM workload, then subtract it from the draw during the workload. Multiply that difference by the relevant hours. This estimates the LLM’s incremental electricity use rather than charging the model for power the computer would have used anyway.
If the computer is dedicated to the LLM
For a dedicated machine that stays on, include both active inference and idle time. A machine that draws modest power while waiting can accumulate meaningful consumption across many hours; counting only the moments when it generates text understates its electricity use.
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What the estimate looks like at a U.S. average rate
The U.S. Energy Information Administration’s July 2026 residential-sector average was 18.31¢ per kWh. That is a national benchmark, not a personalized tariff; the EIA table reports state variation, including 30.49¢/kWh for Massachusetts and 32.41¢/kWh for Maine. Check the EIA’s July 2026 price table for the published figures, and use your own utility bill for a personal estimate. Rates change over time.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errors| Assumed continuous whole-system draw | 30-day energy use | Cost at $0.1831/kWh |
|---|---|---|
| 100 W | 72 kWh | About $13.18 |
| 200 W | 144 kWh | About $26.37 |
| 500 W | 360 kWh | About $65.92 |
These are arithmetic examples using the EIA benchmark, a 30-day month (720 hours), and the stated power assumptions—not measured PC results. To adjust for your rate or schedule, scale the estimate in proportion: half the hours or half the price per kWh produces half the cost, all else equal.
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- EVOLUTION AMD RYZEN AI MAX+ 395 MINI PC - GMKtec EVO-X2 is the next evolution in AI mini PC Ryzen Strix Halo series. Thanks to AMD Simultaneous Multithreading (SMT) the core-count is effectively doubled, to 32 threads. Ryzen AI Max+ 395 has 64 MB of L3 cache and can boost up to 5.1 GHz, depending on the workload. The Ryzen AI Max+ 395 is currently rated as the "most powerful x86 APU" on the market for AI computing.
- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
- AMD RADEON 8090S iGPU GAMING PC - The AMD Radeon RX 8060S offers all 40 CUs with up to 2.9 GHz graphics clock and uses the new RDNA 3.5 architecture. The powerful iGPU is positioned between an RTX 4060 and 4070 laptop GPU and therefore enables gaming in FHD at maximum details in most demanding games. The 8060S can also utilize the full 64GB pool, which is perfect for running LLMs such as Deepseek 32B, which runs comfortably on this machine.
- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 4% better performance in digital content workloads.
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Why local LLM electricity use varies
A preliminary June 12, 2026 benchmark 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. The team sampled GPU draw at 2 Hz with nvidia-smi. It reported 0.2747 joules per output token for Qwen 2.5 0.5B and found that its 7B Mistral result used up to 8.6 times more energy per token than its most efficient tested model. The study is useful evidence that model and workload affect energy use, but its GPU-side measurements are not whole-PC wall power and do not establish a universal monthly bill.
The study also notes that architecture, quantization, and reasoning behavior influence energy use; parameter count alone is not a reliable predictor. In practice, the relevant comparison is not simply “which model uses fewer watts?” It is which model can do the task at acceptable quality, speed, and context length on the hardware you have.
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What to check before choosing a setup
- Compare equivalent work. A small model’s draw is not an apples-to-apples comparison if you need a more capable model or longer context.
- Separate idle from active use. Note whether the host PC would be on regardless, or whether the LLM has a dedicated machine left running.
- Measure at the wall. GPU telemetry describes the GPU, not the combined draw of the computer and power supply.
- Count the actual operating pattern. Short interactive sessions, a model kept loaded, and a server handling requests throughout the day produce different totals.
- Use your actual tariff. Include the applicable marginal price and account for time-of-use pricing where relevant.
- Keep hardware costs separate. The purchase price is an upfront cost; electricity is recurring. A complete ownership-cost comparison needs both, but no current hardware prices are established here.
Memory, model fit, and compatibility affect the choice
NVIDIA’s current guide suggests starting model choices according to available RTX memory tiers of 6–8 GB, 12–16 GB, and 24 GB or more. It recommends choosing a model that fits comfortably in GPU memory. Quantization can reduce memory requirements, sometimes with a quality trade-off, while longer context also uses memory. These are practical fit and capability considerations, not proof that a particular GPU will have the lowest electricity cost. See NVIDIA’s local LLM guide.
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Does running a local LLM mean no ongoing cost?
No subscription or per-use service charge is not the same as zero cost. NVIDIA describes local prompts, files, and context as staying on the user’s machine and on-device usage as having no usage limits or subscription fees; those are vendor statements about its offering, not a guarantee about every app or workflow. Hardware and electricity still cost money, and privacy depends on the software and configuration you use. See NVIDIA’s guide.
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