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Consumer GPU · Ada Lovelace

RTX 4090 rental from €0.59/h

Renting an NVIDIA GeForce RTX 4090 on VRAMfarm costs from €0.59 an hour. It is a consumer card on the Ada Lovelace architecture (2022) with 24 GB of GDDR6X memory: the most popular card for Stable Diffusion, ComfyUI and LLM inference. You pay by the minute from your balance, with no subscription.

To launch, your balance must cover one hour of the machine. You are billed by the minute for actual use.

24 GBGDDR6X memory
1,008 GB/smemory bandwidth
≈82.6TFLOPS FP32
450 WTDP
A large triple-fan graphics card with a braided power cable next to a painting of blue mountains

What the RTX 4090 is good for

LLM inference

24 GB holds a model of up to about 10B parameters in FP16 and up to 36B in 4-bit, for example Qwen 2.5 32B, Gemma 2 27B and Mistral Small 24B. The memory bandwidth of 1,008 GB/s sets how fast tokens are generated.

Which LLMs fit in the 24 GB of the RTX 4090
4-bit, fitsFP16, fitsnot enough memory
Llama 3.1 8B4-bit · 6 GB ≈126 tok/s
Llama 3.1 8BFP16 · 18 GB ≈38 tok/s
Qwen 2.5 14B4-bit · 10 GB ≈72 tok/s
Qwen 2.5 14BFP16 · 31 GB does not fit
Qwen 2.5 32B4-bit · 22 GB ≈32 tok/s
Qwen 2.5 32BFP16 · 71 GB does not fit
Llama 3.3 70B4-bit · 47 GB does not fit
Llama 3.3 70BFP16 · 154 GB does not fit
Memory for the model weights with about 10% headroom for context. Speed is for a single request at 1,008 GB/s of memory bandwidth; with parallel requests in vLLM the total throughput is several times higher. Models that do not fit run on several cards.

Image generation

Good for Flux.1 [dev] at full precision, SDXL with ControlNet and LoRA training without compromises.

Training and fine-tuning

LoRA/QLoRA fine-tuning of models up to 34B parameters and training small networks from scratch.

Rendering

Blender Cycles, Octane and Redshift: 82.6 TFLOPS FP32 and 24 GB of memory for your scenes.

Rental cost by period

PeriodCost
1 hour€0.59
A day (24 hours)€14
A week€99
A month (720 hours)€425

Prices updated 12 November 2026. While the page is open we show the price of the cheapest reliable machine available in the catalogue. Prices are in euros. How VAT applies to your account will be shown at checkout.

Similar GPUs

  • L40: 22% cheaper, 24 GB more memory, FP32 ≈ 90.5 vs 82.6 TFLOPS.
  • RTX A6000: 22% more expensive, 24 GB more memory, FP32 ≈ 38.7 vs 82.6 TFLOPS.
  • L4: 132% more expensive, same memory, FP32 ≈ 30.3 vs 82.6 TFLOPS.

How to rent the RTX 4090

  1. Sign up and top up your balance from €10. To launch, your balance must cover at least one hour of the machine.
  2. In the dashboard, click Rent a GPU, choose what to run (a ready-made app, PyTorch with Jupyter, vLLM or your own Docker image), then the RTX 4090 and a machine.
  3. The server is usually ready in 2–5 minutes. When you’re done, click Stop and billing stops. If you will need your models and results again, click Save and stop: /workspace is kept for €0.03 per GB per month and comes back on your next server.
A gaming PC with a triple-fan graphics card, connected to a laptop

Rental questions

How much does RTX 4090 rental cost?

From €0.59 an hour. Billing is per minute, with a minimum charge of 15 minutes per launch. To launch, your balance must cover one hour of the machine; the rest stays on your balance. Prices updated 12 November 2026.

Which AI models can I run on this card?

24 GB holds an LLM of up to about 10B parameters in FP16 or up to 36B in 4-bit, for example Qwen 2.5 32B, Gemma 2 27B and Mistral Small 24B.

How do I access the rented GPU?

The server is usually ready 2–5 minutes after launch. Ready-made apps for images, video and chat open with a button in the dashboard, and for your own code you get SSH and Jupyter. Developers can choose PyTorch with Jupyter, vLLM, an empty ComfyUI or their own Docker image.

How do I pay?

Top up your balance in euros by card or SEPA transfer, from €10. Payments go through a payment provider that we will announce before launch. VAT, where it applies, is shown at checkout.

How does the RTX 4090 compare with similar cards?

L40: 22% cheaper, 24 GB more memory, FP32 ≈ 90.5 vs 82.6 TFLOPS. RTX A6000: 22% more expensive, 24 GB more memory, FP32 ≈ 38.7 vs 82.6 TFLOPS. L4: 132% more expensive, same memory, FP32 ≈ 30.3 vs 82.6 TFLOPS.

What happens to my data after I stop?

The server disk is wiped when you stop. If you need your models and results, click Save and stop: we keep /workspace for €0.03 per GB per month, and your next server gets it automatically. Saving does not work yet for custom Docker images.

Launch the RTX 4090

Available machines with an hourly price, billed by the minute from your balance.