Local LLM Power Consumption Cost Guide

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TL;DR: Running a 7B model on an RTX 4060 costs roughly $0.02–0.05 per hour in electricity. On an RTX 4090 running a 30B model, it's $0.10–0.15 per hour. For a heavy user running 4–6 hours daily, annual electricity cost is $30–200 depending on your GPU and local rates. Local LLMs cost a few dollars a month — not a meaningful expense for most people.

One question that comes up a lot when people consider switching from cloud AI to local models is whether the electricity cost makes sense. It's a legitimate question. A powerful GPU pulling 300W for hours a day does affect your power bill.

Here's the real math.

GPU Power Draw for LLM Inference

Power consumption during LLM inference is not the same as the GPU's TDP (thermal design power). TDP is the maximum sustained power draw. During inference, especially for smaller models, the GPU often runs at 60–80% of TDP because it's not fully utilizing all CUDA cores — inference is memory-bandwidth bound, not compute bound.

Real measured power draw during active inference:

RTX 4060 8GB:

Idle: ~10–15W
7B model inference: ~80–110W
Rated TDP: 115W

RTX 3060 12GB:

Idle: ~15–20W
7B model inference: ~90–120W
Rated TDP: 170W

RTX 4060 Ti 16GB:

Idle: ~15–20W
7B–14B model inference: ~115–145W
Rated TDP: 165W

RTX 4070 Ti Super:

Idle: ~20–25W
7B–14B model inference: ~150–200W
Rated TDP: 285W

RTX 5070 Ti:

Idle: ~20–25W
7B–14B model inference: ~180–240W
Rated TDP: 300W

RTX 3090:

Idle: ~20–30W
14B–30B model inference: ~220–280W
Rated TDP: 350W

RTX 4090:

Idle: ~25–35W
14B–30B model inference: ~250–320W
Rated TDP: 450W

RTX 5090:

Idle: ~30–40W
30B+ model inference: ~350–450W
Rated TDP: 575W

Note: these are GPU-only numbers. Add 50–80W for the rest of the system (CPU, RAM, storage, PSU overhead) when calculating total rig power.

Electricity Cost Calculation

The formula:

Monthly cost = (watts / 1000) × hours per day × 30 days × cost per kWh

US average electricity rate is approximately $0.16 per kWh as of early 2026. Rates vary significantly: California and Hawaii are $0.25–0.40/kWh, Texas and parts of the Southeast are $0.10–0.13/kWh. Check your utility bill for your actual rate.

Sample calculations at $0.16/kWh:

RTX 4060, running 7B model, 2 hours/day:

100W × 2h × 30 days = 6 kWh/month
6 × $0.16 = $0.96/month

RTX 4070 Ti Super, running 14B model, 4 hours/day:

180W × 4h × 30 days = 21.6 kWh/month
21.6 × $0.16 = $3.46/month

RTX 3090, running 30B model, 6 hours/day:

260W × 6h × 30 days = 46.8 kWh/month
46.8 × $0.16 = $7.49/month

RTX 4090, running 30B model, 8 hours/day (heavy professional use):

290W × 8h × 30 days = 69.6 kWh/month
69.6 × $0.16 = $11.14/month

Full rig (GPU + system) at 400W total, 8 hours/day:

400W × 8h × 30 days = 96 kWh/month
96 × $0.16 = $15.36/month

Cost Comparison: Local vs Cloud AI

This is where the calculation gets interesting for heavy users.

Cloud AI pricing (approximate, early 2026):

ChatGPT Plus: $20/month
Claude Pro: $20/month
Gemini Advanced: $20/month
API usage for heavy users: $50–200+/month

Local AI electricity cost for equivalent use:

Light use (2h/day, mid-range GPU): $1–4/month
Heavy use (8h/day, high-end GPU): $10–20/month

If you're paying $20/month for a single cloud AI subscription and you're a daily heavy user, your local AI electricity bill is likely 10–50% of that cost — and you own the hardware permanently.

The breakeven on hardware depends on what you would have paid in cloud subscriptions. An RTX 3090 at $800 used, saving $15/month in cloud subscriptions vs electricity: breaks even in approximately 53 months. An RTX 4060 at $280, saving $15/month: breaks even in 19 months.

The actual calculation is messier because cloud AI includes model updates, higher quality frontier models, and zero setup time. But for people who want ongoing access to capable AI without recurring costs, the economics can work.

Power Limits and Reducing Draw

One underused technique for reducing electricity costs (and heat and noise) is setting a power limit on your GPU.

NVIDIA cards support power limiting via nvidia-smi:

nvidia-smi -pl 200

This sets a 200W power limit. For inference workloads, which are memory-bandwidth bound rather than compute bound, a 15–20% power reduction often results in only a 5–10% performance drop. You can recover most of the speed reduction with a modest memory clock overclock.

For example, an RTX 4090 with a 350W power limit instead of 450W runs cooler, quieter, and ~22% cheaper in electricity — with approximately 8–10% slower inference speed. For background AI tasks or overnight processing, this tradeoff is often worth it.

The Idle Cost

Don't ignore idle power. If you leave your system running 24/7 for AI availability, the idle consumption adds up:

GPU idle: 15–35W depending on card
Full system idle: 60–120W depending on components

A system idling at 80W running 24/7:

80W × 24h × 30 days = 57.6 kWh/month
57.6 × $0.16 = $9.22/month

If you're running a local AI server that's always on, the idle cost alone can be $7–15/month. Consider a scheduled sleep state or lower-power always-on device (like a Mac Mini or small NUC) for availability with less draw, and bring up the heavy GPU rig for demanding inference tasks.

Summary: Is Electricity Cost a Real Concern?

For most people, no. The math works out to $1–15/month in electricity for realistic local AI usage. That's not a budget line item worth optimizing around.

The cases where it matters:

Running inference 24/7 as a service (electricity starts to be a real variable cost)
High kWh rates ($0.30+/kWh) with heavy GPU use
Running multiple high-TDP GPUs simultaneously

For a solo user running local LLMs as a productivity tool, electricity is a footnote compared to the upfront hardware cost. Run your models, don't worry about the power bill.