Rent an RTX 5060 Ti
NVIDIA GeForce RTX 5060 Ti
Specifications
Renting a RTX 5060 Ti
Rent an RTX 5060 Ti by the minute
The RTX 5060 Ti with 16 GB is the cheapest way into sixteen gigabytes on the current generation. It has the memory of an RTX 5070 Ti and about half its speed, which makes it the card for work that is limited by what fits and not by how fast it runs: a small model kept running, an embedding or classification job, a notebook you leave open. On ParalonCloud you rent the whole machine and pay for the minutes it runs.
A rental opens as Jupyter Lab in your browser, with a terminal, in under a minute. The hourly rate you see when you start is locked for the whole session, billing stops the minute you stop, and there is no contract or minimum spend.
Every RTX 5060 Ti listed here has been benchmarked by us before it is offered, and two of the same card do not measure the same: the two we have verified came out at 385 and 291 GB/s of memory bandwidth. Each machine's own result is on its page.
What fits
What fits in 16 GB
The card has the same 16 GB as an RTX 5070 Ti, so the same things load. What differs is how long they take.
Measured
Verified on RTX 5060 Tis on this network
These are the results of our verification job on two providers' RTX 5060 Tis (a Ryzen 9 7900 and a Ryzen 7 9700X, both Windows machines running WSL2, power limit 180 W), run before each machine was listed. We have not yet published language-model throughput for this card, so there is no tokens-per-second figure here.
Steps
How to rent one
- 01
Pick a machine
Open the rent page and filter for RTX 5060 Ti. Each machine shows its measured bandwidth, PCIe link, power limit, CPU, RAM, free disk, location and reliability score.
- 02
Start the rental
Choose Jupyter Lab, accept the terms and start. You need credits for one hour to open a session and are charged only for the minutes you use.
- 03
Work, then stop
Use the terminal for pip, conda or apt, run your job, download what you want to keep, and stop. Or drive the whole thing from a script with the rental API.
Limits
Know before you rent
- A rental is an unprivileged container: Docker inside it does not work. Install what you need with pip, conda or apt.
- Access is through Jupyter Lab and its terminal. Other ports are not exposed to the internet.
- Everything on the machine is wiped when the rental ends. Download results before you stop, and checkpoint long jobs.
- A spot machine can be reclaimed with two minutes' notice. Use on demand for work that cannot be interrupted.
FAQ
Questions about the RTX 5060 Ti
RTX 5060 Ti or RTX 5070 Ti: which should I rent?
Both have 16 GB, so the same models load on both. On our verification runs the RTX 5070 Ti measured 764 GB/s of memory bandwidth and 92 TFLOPS in FP16, the RTX 5060 Tis 291 to 385 GB/s and 39 to 50 TFLOPS: about half. Rent the RTX 5060 Ti when the job is waiting on you, and the RTX 5070 Ti when you are waiting on the job.
Is the RTX 5060 Ti here the 8 GB or the 16 GB version?
The machines listed on this page report 16,310 MB. The amount of memory each machine reports is on its own page, measured, so you do not have to trust the card's name.
Is 16 GB enough to run an LLM?
For a small one, yes. We serve a 4B model at 4-bit on this card with a 16k context. It is not enough for the 27B class, whose weights alone are 18.7 GB at 4-bit.
How do I know the card is really an RTX 5060 Ti?
Before a machine is listed, a verification job allocates its memory and measures memory bandwidth and compute. The result, and when it was last checked, is on the machine's page.
How much does it cost to rent an RTX 5060 Ti?
The current price is shown at the top of this page and comes from the machines available right now, since providers set their own rate. You are billed per minute at the rate shown when you start, and that rate is locked for the session. There is no contract and no minimum beyond having credits for one hour when you open a session.
Do I get SSH or root on the host?
You get Jupyter Lab with a terminal inside an unprivileged container. You can install packages with pip, conda or apt and use the whole GPU, but you cannot run Docker or open other ports.
Can I rent one from code?
Yes. The rental API lists available GPUs with prices and starts, inspects and stops rentals with an API key. There are also MCP servers for driving it from an AI coding assistant.