Rent an RTX A6000
NVIDIA RTX A6000
Professional workstation GPU with ECC memory.
Specifications
Best for
Renting a RTX A6000
Rent an RTX A6000 by the minute
The RTX A6000 is the 48 GB workstation card: twice the memory of an RTX 4090 on the same Ampere generation as the RTX 3090. It is the card to rent when the model fits a 24 GB card but your context, your batch or your second model does not. On ParalonCloud you rent the whole machine it sits in, not a slice, 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 A6000 listed here has been benchmarked by us before it is offered, and the result is on the machine's page. The two cards in the machine we measured for this page came out at 672 and 663 GB/s of memory bandwidth and 25.0 and 24.6 TFLOPS in FP32.
What fits
What fits in 48 GB
On a 24 GB card the question is how much is left for context once the model has loaded. On 48 GB the model is a small part of the card, and what you are renting is the room around it.
Measured
Measured on RTX A6000s on this network
Measured for this page on 12 October 2026, on one provider machine with two RTX A6000s over PCIe (Xeon Platinum 8380, Ubuntu 24.04), serving Qwen3.8-27B at 4-bit with the model split across both cards, vLLM 0.31, an FP8 KV cache and speculative decoding on. Every answer was exactly 256 tokens, requests went straight to the worker, and each point was run twice after a warm-up. The machine was also serving live traffic, so read these as a floor. Your numbers depend on the host around the card as much as on the card, which is why every machine's own benchmark is on its page.
Steps
How to rent one
- 01
Pick a machine
Open the rent page and filter for RTX A6000. Each machine shows its measured bandwidth, PCIe link, power limit, CPU, RAM, free disk, location and reliability score, and how many cards it has.
- 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.
- The RTX A6000 is an Ampere card from 2020. For the same model it is slower per request than a current RTX 5090; you rent it for the memory.
FAQ
Questions about the RTX A6000
How much does it cost to rent an RTX A6000?
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.
RTX A6000 or RTX 4090: which should I rent?
The RTX 4090 is the faster card and has 24 GB. The RTX A6000 is a generation older and has 48 GB. If your model and context fit in 24 GB, the RTX 4090 finishes sooner. If they do not, the RTX A6000 runs what the RTX 4090 cannot load: on our own serving setup a 27B model gets a 32k context on a 24 GB card and 131k on a 48 GB one.
Can an RTX A6000 run a 27B or 30B class model with a long context?
Yes. At 4-bit the weights of a 27B model take 18.7 GB, which leaves more than half of a 48 GB card for context. On two RTX A6000s with the model split across them we serve the full 262,144-token window, and measured 48.4 seconds to the first token on a 93,369-token prompt, 0.7 seconds when the same prompt was sent again.
How fast is an RTX A6000 for LLM inference?
On a machine with two RTX A6000s serving a 27B model at 4-bit we measured 65 to 76 tokens per second for a single request, about 430 in total at 8 concurrent requests and about 815 at 32. That machine was serving other traffic at the time, so these are a floor, and a single card will be lower.
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.
How do I know the card is really an RTX A6000?
Before a machine is listed, a verification job allocates its memory and measures memory bandwidth and compute. The RTX A6000s in the machine we measured came out at 672 and 663 GB/s and about 25 TFLOPS in FP32 each; a card that is not what it claims does not get near that. The result, and when it was last checked, is on the machine's page.
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.