Is it cheaper to rent or buy a GPU for local AI?

It depends entirely on how many hours a month you actually use it. Below roughly 4-8 hours a day of sustained use, cloud rental is usually cheaper over a GPU's useful life. Above that, a used RTX 3090 typically breaks even within 1-3 years. Run your own hours through /tools/local-vs-cloud/ rather than trusting a flat rule.

How many hours until a used RTX 3090 pays for itself vs renting a 4090?

Using observed prices (used 3090 ~$600-800, 4090 spot rental ~$0.20-0.37/hr, both 2026), the math works out to roughly 2,500-4,000 hours of usage before the 3090's purchase price is offset by rental costs avoided. That's about 3.5-5.5 months of continuous (24/7) use, or 1-3 years at several hours a day.

Does electricity cost matter in the rent vs buy decision?

Yes, and most informal comparisons skip it. A 3090 under sustained load draws roughly 300-350W; at a U.S. average residential rate near $0.15/kWh (EIA, 2025), that's about $0.045-0.05 per hour of active inference, plus a smaller but real idle-power cost if the machine is left on. It doesn't change the conclusion by itself, but omitting it flatters the buy case.

What about depreciation and resale value?

Resale data for used GPUs is thin and volatile, so treat any purchase conservatively: assume the card retains little resale value by the time you'd sell it, rather than counting on a specific resale percentage. If you can sell for something later, that only shortens the break-even point you calculate assuming zero resale.

Does cloud rental have hidden costs beyond the hourly rate?

Often, yes. Storage while an instance is stopped, data egress fees, and idle billing if you forget to terminate an instance can all add up. These vary by provider — see the per-provider reviews before assuming the advertised hourly rate is the whole story.

Rent vs Buy a GPU for Local AI: The Break-Even Math Nobody Shows You

Is it cheaper to rent a GPU or buy one for local AI?

There is no flat answer — it is a break-even calculation, and the honest version depends on how many hours a month you actually put the GPU to work. Sustained daily inference (several hours a day, most days) tends to favor buying a used RTX 3090. Bursty, occasional, or seasonal workloads tend to favor renting. Anyone who gives you a single winner without asking about your usage pattern is skipping the math — which is exactly what most “rent vs buy” content online does. This article builds the model instead of asserting a conclusion, and the interactive version of it lives at the local-vs-cloud calculator, where you can plug in your own hours instead of trusting anyone’s assumed usage pattern, including ours.

The reason this matters: a calculator that leaves out electricity and depreciation isn’t simplified, it’s wrong in the direction that flatters whichever side the author wants you to pick. If you’re comparing options, insist on seeing all four cost buckets — purchase price, electricity, depreciation, and (on the cloud side) idle/egress fees — before you trust any number.

What actually goes into the buy-side cost?

Buying a GPU isn’t just the sticker price. Three costs stack on top of it, and skipping any one of them understates what ownership actually costs.

Purchase price. A used RTX 3090 24GB runs roughly $600-800 on eBay (observed 2026-06-29) — this is the community-cited sweet spot for VRAM-per-dollar, covered in depth in the used RTX 3090 buying guide. New cards (a 4090, say) cost meaningfully more up front, which raises the break-even bar proportionally — the math below is easy to rerun with a different purchase price.

Electricity. A 3090 draws roughly 300-350W (nvidia.com TDP spec) under sustained inference load. At the U.S. average residential electricity rate of roughly $0.15/kWh (EIA, 2025), that’s approximately:

0.32 kW × $0.15/kWh ≈ $0.048 per hour of active inference

That number looks small, and per-hour it is — but it is not zero, and a machine left running 24/7 also draws idle power (fans, standby, the rest of the box) even when nothing is generating tokens. A rig idling at ~20W around the clock adds roughly $0.07/day, or about $2/month, purely for being powered on and doing nothing. That’s a real cost of the “always-on homelab” pattern that a pure per-hour model misses.

Depreciation. This is the piece most rent-vs-buy posts skip entirely, and it’s the one Persona-6-style buyers care about most: what is the card worth when you’re done with it? Used-GPU resale data is thin and volatile enough that inventing a specific resale percentage would be dishonest. The conservative, honest approach: assume the card retains little resale value by the time you’d actually sell it, and treat the full purchase price as sunk cost in your break-even math. If you do resell it for something, that only improves your real-world result versus the estimate — it never makes the estimate look worse.

What actually goes into the rent-side cost?

Cloud GPU rental looks simpler — an hourly rate — but the full picture has more moving parts than the advertised number.

The hourly rate. RTX 4090 spot instances have been observed in the ~$0.20-0.37/hr range (community-cited, provider pricing pages, 2026 — not independently verified by LocalRig). This is the closest cloud-market equivalent to a local 3090/4090-class card; rental markets skew toward newer cards, so this is the honest comparison point even though the local side of this article centers on the 3090. See the cheapest RTX 4090 cloud rental roundup for current per-provider pricing.

Idle and storage billing. Some providers bill storage or a reduced idle rate while an instance is stopped but not terminated; some charge for attached storage regardless of compute state. If you forget to terminate an instance, “renting for an hour” can quietly become “renting all weekend.”

Egress fees. Moving model weights, datasets, or generated output off the provider’s network can carry a per-GB egress charge on some platforms. This is workload-dependent — a chat session generates almost nothing to egress, but pulling large fine-tuned checkpoints back to your own machine can add real dollars. Check the specific provider’s pricing page; it varies enough between providers that a general number here would mislead more than help.

No electricity or depreciation math required. This is cloud’s structural advantage: you rent capacity by the hour and hand the hardware risk back to the provider. That’s real value for bursty use — you just also pay for it in the per-hour markup baked into the rate.

How do you calculate the actual break-even point?

The break-even hour count is the purchase price (net of any expected resale, conservatively assumed at zero) divided by the difference between the cloud hourly rate and your local per-hour running cost:

t* = Purchase price ÷ (Cloud $/hr − Local $/hr)

Working it with the ranges above — used 3090 at $600-800, 4090 rental at $0.20-0.37/hr, local running cost at ~$0.048/hr:

Low end: $600 ÷ ($0.20 − $0.048) = $600 ÷ $0.152 ≈ 3,947 hours
High end: $800 ÷ ($0.37 − $0.048) = $800 ÷ $0.322 ≈ 2,484 hours

So the break-even point lands somewhere around 2,500-4,000 hours of use — not a single number, a range, because both purchase price and rental rate are themselves ranges. Translated into daily usage patterns:

Usage pattern	Hours to break even	Time to break even
Continuous (24/7)	2,500-4,000 hrs	~3.5-5.5 months
8 hrs/day	2,500-4,000 hrs	~10-16 months
4 hrs/day	2,500-4,000 hrs	~1.7-2.7 years
2 hrs/day	2,500-4,000 hrs	~3.4-5.5 years
1 hr/day or bursty/seasonal	2,500-4,000 hrs	rarely reached within the card’s useful life

Read the last row carefully: at light or bursty usage, the break-even point may never arrive before the card is effectively obsolete or the workload changes. That is not a hedge — it is the honest output of the same formula that favors buying at higher utilization. The exact numbers move with your real electricity rate, the rental price you can actually book, and the purchase price you pay, which is why this is a calculator problem, not a blog-post-conclusion problem. Run your own numbers at the local-vs-cloud calculator instead of eyeballing the table above.

So who should buy, and who should rent?

Buy a used RTX 3090 if: you run inference most days, for multiple hours a day — a personal assistant, a coding companion, a document pipeline you lean on daily. At that utilization the math above breaks even within roughly a year to three years, well inside the card’s realistic useful life, and after break-even every additional hour is nearly free (just the ~$0.05/hr electricity). This is also the buyer for whom the hardware buying framework and the full best-GPU guide matter — the 3090 remains the community-cited price/VRAM standard.

Rent if: your usage is bursty, seasonal, exploratory, or you need a card class (a 4090, or something bigger) you’re not ready to own outright. Renting also wins if you cannot tolerate the depreciation risk, don’t want to manage hardware, or need to burst to a different GPU tier for a single project. The honest reading of the homelab-vs-cloud debate (r/LocalLLaMA and adjacent communities, 2025-2026) is that renting wins by a wide margin above single-GPU scale — multi-GPU clusters, training runs, or anything that needs to scale up and back down again — because cloud absorbs that elasticity and a home rig cannot.

Bottom line

There is no universal winner here, and any article claiming one is skipping either electricity, depreciation, or both. The formula is simple once the inputs are honest: purchase price divided by the gap between what you’d pay to rent and what it costs you per hour to run locally. At sustained daily use, that gap closes in well under the card’s useful life and a used 3090 is the better dollar-for-dollar bet. At light or unpredictable use, cloud rental’s per-hour price — plus zero depreciation risk — wins comfortably. The honest move is to actually count your hours before you buy or commit to a rental budget: use the local-vs-cloud calculator with your own electricity rate and expected usage rather than borrowing anyone else’s assumptions, including the ones in this article.