TL;DR: A 24/7 RTX 3090 LLM server on a realistic solo duty cycle burns ~774 kWh/year — about $125/year in Texas, $142 at the US average, $252 in California, and roughly €286 in Germany at September 2026 residential rates. Idle hours, not generation hours, dominate the bill. Above ~30¢/kWh, always-on stops making sense for most workloads.

	Texas (16.2¢)	US average (18.3¢)	California (32.6¢)	Germany (~37 €ct)
RTX 3090 box, idle 24/7	~$85/yr	~$96/yr	~$171/yr	~€195/yr
Same box, 2h generation/day	~$125/yr	~$142/yr	~$252/yr	~€286/yr
Same box, 8h agent workloads/day	~$246/yr	~$278/yr	~$495/yr	~€562/yr
Mac mini M4 Pro, 2h load/day	~$23/yr	~$26/yr	~$46/yr	~€53/yr

Honest take: In Texas or at the US average rate, run the box 24/7 and don’t think about it — it’s a $10/month habit. In California, Germany, or anywhere else above 30¢/kWh, either move the always-on role to a low-idle machine like a Mac mini or stop paying for idle hours entirely and rent GPU time when you need it.

Every self-hosting budget thread quotes the GPU’s TDP, multiplies by 8,760 hours, and arrives at a terrifying number that nobody actually pays. A 350W TDP RTX 3090 does not draw 350W around the clock — it draws ~22W with a model parked in VRAM and only spikes during generation. This article does the math with measured wall-power numbers and September 2026 residential rates, region by region, so you can see exactly where always-on self-hosting is cheap, where it’s a standing bill that rivals API costs, and what to do about it. The full ownership picture (hardware, maintenance hours) is in the true year-one cost of self-hosting an LLM; this piece isolates the electricity line.

How many watts does a 24/7 AI server actually draw?

Measured at the card, an RTX 3090 running a local LLM stack draws ~22W at idle with a model loaded and ~310W mid-generation — not the 350W TDP on the box. Check your own card instead of trusting spec sheets:

$ nvidia-smi --query-gpu=power.draw --format=csv,noheader
22.41 W     # model loaded in VRAM, no requests
309.87 W    # mid-generation, 30B-class model at Q4

Add ~40W for the rest of a modest system (CPU at idle, RAM, SSD, fans, PSU losses) and you get the numbers that matter — whole-system wall draw:

• Near-idle, model resident: ~60W total
• Generating: ~400W total
• Card stuck in a high P-state (a real failure mode, covered below): 100W+ at “idle”

From those, three realistic 24/7 duty cycles:

Duty cycle	Daily energy	Annual energy
Idle appliance (always ready, rarely used)	1.44 kWh	~526 kWh
Solo daily driver (2h generation, 22h near-idle)	2.12 kWh	~774 kWh
Agent workhorse (8h generation, 16h near-idle)	4.16 kWh	~1,518 kWh

The middle row matches what a single developer actually does with a local Qwen or Llama model — bursts of chat and coding assistance, long stretches of nothing. Note the uncomfortable ratio in that row: 68% of the energy is idle time. You pay most of the bill for the option to run inference, not for inference.

For contrast, Apple’s published figures for the Mac mini M4 Pro are ~5W at idle and ~140W at maximum load. Two hours a day at full tilt plus 22 idle hours is ~0.39 kWh/day — ~142 kWh/year, less than a fifth of the 3090 box’s solo-driver figure, which is why low-idle machines keep winning the always-on role in mixed home labs.

What does residential electricity cost by region in September 2026?

The US average residential rate is ~18.3¢/kWh as of September 2026 (EIA), but the spread between regions is wide enough to flip the self-hosting verdict:

Region	Residential rate	Verified
Texas	16.2¢/kWh	Sep 2026
US average	18.3¢/kWh	Sep 2026 (EIA)
US Northeast (regional avg)	25.5¢/kWh	Sep 2026
California	32.6¢/kWh	Sep 2026
Germany (household avg)	~35.6–37 €ct/kWh	Q2 2026 (BDEW)

Two caveats that matter for this audience. First, US rates rose ~5% year-over-year in 2026 and hit an all-time-high national average in April — the trend is up, so bake a few percent of annual growth into any multi-year break-even math. Second, the German figure is the household average; new-customer market tariffs run as low as ~24–27 €ct/kWh, while default-supplier tariffs reach ~42 €ct. If you’re in Germany and self-hosting, which tariff you’re on changes the bill by ±40%.

Time-of-use plans cut the other way: California’s TOU off-peak rates are meaningfully below the 32.6¢ average, so scheduling batch inference and fine-tuning overnight recovers some of the gap — but it does nothing for the idle floor, which runs through peak hours too.

What does a year of always-on AI actually cost in each region?

Multiply the measured duty cycles by the verified rates and the regional spread becomes stark — the identical box costs 2–3× more to keep alive in California or Germany than in Texas:

Annual cost	Idle appliance (526 kWh)	Solo driver (774 kWh)	Agent workhorse (1,518 kWh)
Texas	$85	$125	$246
US average	$96	$142	$278
US Northeast	$134	$197	$387
California	$171	$252	$495
Germany (37 €ct)	€195	€286	€562

Three readings of that table:

• At Texas or US-average rates, electricity never decides the question. Even the 8-hour agent workload costs less than a dollar a day. Whether self-hosting makes sense is settled by hardware cost and your time, not the meter — see when NOT to self-host AI for that side.
• At California rates, the agent workhorse costs ~$495/year in electricity alone. For comparison, renting an RTX 3090 on Vast.ai starts around $0.07/hr (market price, September 2026): the same 2,920 GPU-hours per year rented on demand start at ~$204 — and the rented card costs nothing while you sleep.
• At German household rates, even the idle appliance is ~€195/year. That’s a standing bill for a machine that’s mostly waiting. Twenty euros a month buys a lot of open-weight API tokens in 2026.

When is an always-on AI server the wrong call?

Don’t run a GPU box 24/7 if any of these describe you:

• Your rate is above ~30¢/kWh and your usage is bursty. California and most of Western Europe fall here. The idle floor alone (€195–€562/year depending on duty cycle in Germany) rivals or exceeds what the same work costs on rented GPUs or open-weight APIs. Wake the box when you need it, or rent.
• You use it heavily but on a schedule. Nightly batch jobs, weekend fine-tuning runs, monthly re-embedding of a document corpus — none of that needs 8,760 hours of uptime. A machine that’s on 10 hours a week pays ~6% of the always-on bill.
• The always-on role is really “small model, light duty.” If what runs 24/7 is a 7B assistant, home-automation intent parsing, or an embedding endpoint, a 60W-idle GPU tower is the wrong appliance. A Mac mini-class box or an iGPU mini-PC idles at single-digit watts and handles that load.
• You haven’t measured your box yet. TDP-based estimates run 2–4× high, and a misconfigured box (see the P-state trap below) runs 2–3× above a healthy one. Decisions made on either wrong number are wrong decisions.

The one profile where always-on is unambiguous: cheap power, daily interactive use, privacy requirements that rule out cloud inference. At 16–18¢/kWh, $10–12/month is a rounding error next to the maintenance hours you’ll actually spend.

How do you cut the bill without shutting the server down?

Power-limiting the GPU is the highest-value single change: capping an RTX 3090 from 350W to 280W costs under 1% of tokens/sec in LLM inference (community benchmarks, reproduced repeatedly on r/LocalLLaMA):

$ sudo nvidia-smi -pl 280
Power limit for GPU 00000000:01:00.0 was set to 280.00 W from 350.00 W.

That ~70W cut only applies during generation, so the yearly saving scales with duty cycle: ~$9/year at US rates for the solo driver, but ~$37/year (US) to ~€76/year (Germany) for the 8-hour agent workload. Set it in a systemd unit or crontab @reboot line — it resets on reboot.

The second lever is the idle floor itself:

• Check for the stuck P-state trap. Some inference stacks hold GPU clocks high after a request finishes, so the card “idles” at 100W+ instead of 22W. If nvidia-smi shows a suspicious idle draw, query nvidia-smi -q -d PERFORMANCE — a card parked in P2 instead of P8 usually means a client is keeping a context alive (a stuck web UI tab polling the API is the classic culprit). This one bug roughly triples the annual figures in the table above.
• Let Ollama unload models. OLLAMA_KEEP_ALIVE=10m frees VRAM after ten idle minutes; an empty card idles measurably lower than one holding 20GB of weights. The cost is a few seconds of reload latency on the next request.
• Suspend-plus-Wake-on-LAN for scheduled workloads. A suspended box draws ~1–2W. If your usage is evenings-only, 16 suspended hours a day cuts the idle floor by two-thirds. The catch: cheap consumer boards sometimes drop WoL after firmware updates — test it before you rely on it.
• Move the 24/7 role off the GPU tower. Run the always-on lightweight services (embeddings, small-model chat, home automation) on a low-idle box and wake the GPU tower for heavy jobs. GPU selection for the tower side is covered in the GPU buying guide for local AI on our hardware sister site.

What to actually buy

Prices as of September 2026, taken from the analysis above:

Your situation	The move	Cost	Where
Sub-20¢/kWh power, daily LLM use	Used RTX 3090 box, run it 24/7	~$1,050 + ~$96–142/yr power	Check price
30¢+/kWh power, light always-on duty	Mac mini M4 Pro as the 24/7 node	~€53/yr power (hardware price varies)	Check price
Bursty heavy jobs, expensive power	Rent a 3090 by the hour, own nothing idle	from $0.07/hr	Vast.ai
Not sure what your box draws	Wall-socket power meter, measure a week	under $40 typically	Check price

The meter row is the one most people skip and shouldn’t: every figure in this article came from wall measurements, and a $30 meter has caught more than one 100W-idle misconfiguration that a spec sheet said couldn’t exist.

FAQ

How much does it cost to run an LLM server 24/7 in 2026?

At the September 2026 US average rate of 18.3¢/kWh, a used RTX 3090 system on a realistic solo duty cycle (~774 kWh/year measured, not TDP-derived) costs about $142/year. Texas comes in around $125, California around $252, and Germany around €286 at the ~37 €ct/kWh household average.

Does an idle GPU really use much power?

Less than most people assume at the card, more than they assume at the wall. An RTX 3090 idles at ~22W with a model loaded, but the whole system draws ~60W — and on a 24/7 solo-use box, those idle hours are roughly two-thirds of the annual energy. Misconfiguration matters more: a card stuck in a high P-state idles at 100W+, tripling the bill.

Is it cheaper to rent a GPU than to run one at home?

Above roughly 30¢/kWh, often yes for bursty workloads. In California, eight hours of daily generation on an owned RTX 3090 costs ~$495/year in electricity alone, while the same annual GPU-hours rented on Vast.ai start at ~$204 with zero idle cost. Below 20¢/kWh, owning wins on economics whenever the hardware is already justified.

Sources

• EIA Electric Power Monthly — US residential electricity prices
• Electric Choice — electricity rates by state, September 2026
• Clean Energy Wire — what German households pay for electricity (BDEW data)
• RTX 3090 power limit benchmarks for local LLM inference
• Apple — Mac mini power consumption specifications

Recommended Gear

• RTX 3090 24GB (used) — the 24GB workhorse this article’s measurements are based on
• Mac mini M4 Pro — the low-idle always-on node for expensive-power regions
• Kill A Watt power meter — measure your real wall draw before trusting anyone’s table, including this one

What self-hosting actually costs

Real cost breakdowns for self-hosted AI: hardware floors, power, maintenance hours, and the honest comparison against paying for it. No spam, unsubscribe anytime.

Email address Subscribe