Rent an NVIDIA RTX 3070 GPU with 8 GB VRAM
Rent RTX 3070 cloud GPU servers by the hour on Cloudj: 8 GB VRAM, NVIDIA Ampere and about 19.8 TFLOPS FP32 at a 220 W power limit. It is a fast 8 GB card for developers and creators whose jobs are compute-bound rather than memory-bound: SD 1.5, vision models, 7B LLMs at 4-bit and rendering previews. Pick a live configuration, start from the Ubuntu 22.04 + CUDA or PyTorch + JupyterLab template and connect over SSH with your key.
RTX 3070 specifications
VRAM per GPU
8 GB
Architecture
NVIDIA Ampere
Compute capability
8.6
FP32 compute
≈ 19.8 TFLOPS
Memory bandwidth
≈ 386 GB/s
Power limit
220 W
Values reported by the infrastructure provider for machines currently listed; real performance varies by machine.
Live RTX 3070 configurations and prices
Out of stock right now
Browse available GPUsWhat you can run on the RTX 3070 cloud GPU
Quick SD 1.5 iterations
19.8 TFLOPS FP32 turns around SD 1.5 batches fast when you are testing prompts and styles.
Blender and GPU rendering previews
Render lookdev and preview frames on Ampere RT hardware for scenes that fit in 8 GB.
Real-time vision inference
Run detection and tracking models on video streams with low latency.
7B LLMs at 4-bit
Serve quantized 7B–8B models for single-user chat or app development.
Is the RTX 3070 the right GPU for your workload?
Choose the RTX 3070 for code testing, lightweight inference, and small-scale image generation that fits comfortably inside 8 GB of memory. Select a GPU with more VRAM for fine-tuning or large model weights, or step down to an entry-level card if your workload requires minimal compute.
Guides for RTX 3070
RTX 3070 cloud GPU: frequently asked questions
Rent the RTX 3070 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.