Rent an NVIDIA RTX 3090 GPU with 24 GB VRAM
Rent RTX 3090 cloud GPU servers by the hour on Cloudj: 24 GB VRAM, NVIDIA Ampere and about 35.4 TFLOPS FP32 with 805 GB/s of memory bandwidth. It remains a favorite 24 GB card for AI builders: Flux and SDXL, 8B LLMs in 16-bit, 30B-class models at 4-bit and QLoRA fine-tuning, at a 350 W power limit. Pick a live configuration, start from the Ubuntu 22.04 + CUDA or PyTorch + JupyterLab template and connect over SSH with your key.
RTX 3090 specifications
VRAM per GPU
24 GB
Architecture
NVIDIA Ampere
Compute capability
8.6
FP32 compute
≈ 35.4 TFLOPS
Memory bandwidth
≈ 805 GB/s
Power limit
350 W
Values reported by the infrastructure provider for machines currently listed; real performance varies by machine.
Live RTX 3090 configurations and prices
From $0.25/h
incl. 32 GB disk · Live price, updated every 15 minutes
What you can run on the RTX 3090 cloud GPU
Flux and SDXL in ComfyUI
24 GB runs Flux in FP8 or near-full precision and SDXL with multiple LoRAs and ControlNets.
30B-class LLMs at 4-bit
Fit a quantized 30B–34B model in Ollama or vLLM, or an 8B model in 16-bit.
QLoRA fine-tuning
Fine-tune 7B–14B language models with Unsloth in BF16 on a single card.
Video generation tests
Try video models in ComfyUI that need 24 GB to load at all.
Is the RTX 3090 the right GPU for your workload?
The RTX 3090 suits developers and researchers who need 24 GB of VRAM for memory-intensive tasks on a consumer-segment Ampere card. Choose a smaller GPU for lightweight inference that requires less memory, or step up to a workstation or datacenter GPU if your models exceed 24 GB of VRAM or require newer architecture features.
Guides for RTX 3090
RTX 3090 cloud GPU: frequently asked questions
Rent the RTX 3090 cloud GPU by the hour
Pick a live configuration, choose a template and connect over SSH with your key. Billing stops when you destroy the instance.