Rent GPU Compute RTX 4090, A100, H100
Deploy GPU instances in 30 seconds. Pre-installed CUDA, PyTorch, TensorFlow. Perfect for AI training, ML inference, rendering, and research.
What is GPU Compute?
GPU Compute Explained
GPU Compute refers to using Graphics Processing Units (GPUs) for general-purpose computing tasks beyond graphics rendering. GPUs excel at parallel processing, making them ideal for AI training, machine learning inference, scientific simulations, and 3D rendering.
Available GPU Types
RTX 3090
24GB GDDR6X
$0.25/hr- Budget-friendly
- Development
- Small models
RTX 4090
24GB GDDR6X
$0.39/hr- Best value
- Inference
- Fine-tuning
A100 80GB
80GB HBM2e
$3.76/hr- Large models
- Training
- Research
H100 80GB
80GB HBM3
$6.62/hr- GPT training
- Transformer Engine
- FP8 precision
L40
48GB GDDR6
$1.96/hr- Rendering
- Video encoding
- Inference
A6000
48GB GDDR6
$0.42/hr- Professional
- CAD/3D
- Visualization
GPU Compute Use Cases
AI/ML Training
Train deep learning models, fine-tune LLMs, and run distributed training across multiple GPUs.
- ✓ PyTorch & TensorFlow pre-installed
- ✓ Multi-GPU support (up to 8x A100)
- ✓ NVLink for fast GPU-to-GPU
- ✓ Persistent storage for checkpoints
ML Inference
Deploy models for real-time inference with low latency and high throughput.
- ✓ vLLM, TGI, Triton ready
- ✓ Auto-scaling available
- ✓ Low-latency networking
- ✓ Cost-effective RTX 4090
3D Rendering
Render complex 3D scenes, animations, and visual effects with GPU acceleration.
- ✓ Blender, Maya, 3ds Max
- ✓ NVIDIA OptiX ray tracing
- ✓ High VRAM for large scenes
- ✓ Remote desktop available
Quick Start Commands
Check GPU Status
nvidia-smiTest PyTorch GPU
python -c "import torch; print(torch.cuda.is_available())"Start Jupyter Lab
jupyter lab --ip=0.0.0.0 --allow-rootRun Training Script
torchrun --nproc_per_node=4 train.pyPre-installed Software
Frequently Asked Questions
What is GPU Compute?
GPU Compute refers to using Graphics Processing Units (GPUs) for general-purpose computing tasks beyond graphics rendering. GPUs excel at parallel processing, making them ideal for AI training, machine learning inference, scientific simulations, and 3D rendering.
How much does GPU rental cost?
VoltageGPU offers competitive GPU rental pricing: RTX 4090 (24GB) from $0.39/hour, A100 80GB from $3.76/hour, H100 80GB from $6.62/hour. Multi-GPU configurations available for large-scale training.
What GPUs are available?
VoltageGPU offers a wide range of GPUs: Consumer (RTX 3090, RTX 4090), Professional (A100 40GB/80GB, A6000), Enterprise (H100 80GB, H200). All GPUs come with CUDA, PyTorch, TensorFlow pre-installed.
How fast can I deploy?
GPU instances deploy in 30-60 seconds on VoltageGPU. Simply select your GPU, choose a template (PyTorch, TensorFlow, etc.), and click deploy. SSH access and Jupyter notebooks are available immediately.
What can I use GPU compute for?
GPU compute is used for: AI/ML model training, deep learning inference, LLM fine-tuning, 3D rendering (Blender, Maya), video encoding, scientific simulations, and any CUDA-accelerated workloads.
Is my data persistent?
Yes, all pods include persistent NVMe storage. Your data, models, and configurations are preserved even when the pod is stopped. Resume within 7 days to keep the same GPU allocation.
Start Using GPU Compute Today
Get $5 free credit. No credit card required. Deploy in 30 seconds.