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. CompSniper, fetched 2026-10-06, reports an eBay sold median of $1,499 across 204 sales, with a typical band of $1,329–$1,599 and a latest sale that day. The recent table includes some RTX 3090 Ti cards, so this is the tracker’s published median, not a 3090-only recount. The Rent-vs-Buy Index uses the midpoint of that typical band, $1,464, which is not the median and not RigPrice’s $1,655 asking rate the same day. The June 29, 2026 listing band of $600–$800, and the August 1, 2026 sold band of $580–$780, stay in the price spine as history. Card-only, $1,499 / 24 GB is $62.46/GB. That is the value pick against a used 4090 when the model fits in 24GB, not against every whole computer. The used RTX 3090 buying guide has the comparison. A higher purchase price raises the break-even bar in proportion.
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. The price spine’s latest RTX 4090 rental row is $0.19–$0.35/hr, observed 2026-08-01 from RunPod and Vast.ai pricing pages. It was not re-fetched on 2026-10-06. The June 29, 2026 community band of $0.20–$0.37/hr is the older row. Rental markets skew toward newer cards, so a 4090 hour is the comparison this page uses even though the buy side is a 3090. See the cheapest RTX 4090 cloud rental roundup for per-provider pricing. Consumer 24 GB try-paths: RunPod and Vast.ai.
Datacenter GPUs are a different rental. The 4090 marketplace rate answers “will this GGUF fit a 24 GB card?” Models that need ~75–80 GB of GPU memory (Qwen 3.8 Flash GGUF 1-bit, many 70B+ weights at higher quants) are an A100/H100 hour, not a 4090 SKU. Check Vultr GPU Cloud for current A100/H100 inventory. LocalRig is not copying a Vultr hourly rate onto this page. Break-even math below still uses the 4090 rental as the consumer-card comparison.
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)
The index number uses one pair of midpoints. Purchase midpoint ($1,329 + $1,599) / 2 = $1,464. Rental midpoint ($0.19 + $0.35) / 2 = $0.27/hr. Local running cost stays $0.048/hr (320 W × $0.15/kWh). Gap = $0.27 − $0.048 = $0.222/hr.
6,595 hours = $1,464 ÷ $0.222, rounded. That is the figure on the Rent-vs-Buy Index. The sold median is a different input: $1,499 ÷ $0.222 ≈ 6,752 hours. Neither figure is an asking-price calculation.
The corners of the two bands, same electricity:
- Short end: $1,329 ÷ ($0.35 − $0.048) = $1,329 ÷ $0.302 ≈ 4,401 hours
- Long end: $1,599 ÷ ($0.19 − $0.048) = $1,599 ÷ $0.142 ≈ 11,261 hours
The June worksheet ($600–$800 against $0.20–$0.37/hr, about 2,500–4,000 hours) is history. The October card is about double that purchase price, and the rental row was not refreshed, so the hour count moved with the card.
| Usage pattern | Index midpoint (6,595 hrs) | Band corners (4,401–11,261 hrs) |
|---|---|---|
| Continuous (24/7) | ~9.0 months | ~6.0–15.5 months |
| 8 hrs/day | ~2.3 years | ~1.5–3.9 years |
| 4 hrs/day | ~4.5 years | ~3.0–7.7 years |
| 2 hrs/day | ~9.0 years | ~6.0–15.4 years |
| 1 hr/day or bursty/seasonal | rarely reached | rarely reached |
Calendar time uses 365 days/year and 30.44 days/month. The index page’s own labels match the midpoint column.
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 many hours a day, and you can live with a multi-year payback. At the index midpoint, 8 hours a day reaches 6,595 hours in about 2.3 years. Four hours a day takes about 4.5 years, which is no longer the June “inside a year to three years” reading. After break-even, extra hours cost about $0.05/hr in electricity. The best-GPU guide is the buyer for whom 24GB is enough and the alternative is a used 4090, not a claim that this card is the cheapest memory on the market.
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. The formula is purchase price divided by the gap between the cloud hourly rate and local electricity. At the October 6, 2026 sold-range midpoint and the August 1, 2026 rental midpoint, that gap closes in about 6,595 hours. Heavy daily use can still get there inside a few years. Four hours a day is about 4.5 years, and light use may never get there. The June 2,500–4,000 hour band assumed a $600–$800 card. Count your own hours in the local-vs-cloud calculator, which reads the same price spine, and watch the monthly series on the Rent-vs-Buy Index.
Frequently Asked Questions
Is it cheaper to rent or buy a GPU for local AI?
It depends on hours of use. The Rent-vs-Buy Index uses the midpoint of CompSniper's October 6, 2026 sold typical band, $1,464, against the August 1, 2026 RTX 4090 rental midpoint of $0.27/hr. That break-even is about 6,595 hours: about 2.3 years at 8 hours a day, and about 4.5 years at 4 hours a day. The June reading of roughly 1–3 years above 4–8 hours a day used a $600–$800 card. Run your own hours through /tools/local-vs-cloud/.
How many hours until a used RTX 3090 pays for itself vs renting a 4090?
Using the index inputs above, about 6,595 hours. The sold median itself is $1,499, which is about 6,752 hours at the same $0.27/hr rental midpoint and $0.048/hr local electricity. A worked range on the typical sold band ($1,329–$1,599) against the August rental band ($0.19–$0.35/hr) is about 4,401–11,261 hours. The August rental rate was not refreshed on October 6. Continuous use of the 6,595-hour midpoint is about 9 months, not the June 3.5–5.5 months.
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.