•● What Is RunPod GPU Cloud?
Explore RunPod GPU cloud computing for AI development, machine learning, model training, and high-performance computing, with on-demand GPU resources designed to support demanding workloads and flexible development workflows.
RunPod was founded by Zhen Lu and Pardeep Singh, who began their journey with GPU hardware in New Jersey in late 2021. The platform has since evolved into an AI-focused cloud computing environment built to help developers access accelerated computing resources without the need to purchase and maintain their own GPU infrastructure.
Through its cloud platform, RunPod offers several computing models, including GPU Pods for dedicated GPU instances, Serverless for deploying inference workloads, and GPU Clusters for distributed computing tasks. These options support different technical requirements, from interactive development and model fine-tuning to production inference and multi-GPU workloads.
RunPod GPU infrastructure is designed for developers, AI engineers, researchers, and teams working with deep learning, generative AI, image generation, and other GPU-intensive applications. With configurable environments and developer tools such as APIs, command-line utilities, and integrations for automated workflows, users can build, test, and deploy AI applications according to their project requirements.
•● Runpod GPU Cloud Instances
Compare RunPod GPU configurations to find the right combination of NVIDIA GPU performance, VRAM capacity, and storage for your AI workloads. From model training and fine-tuning to image generation and inference, explore flexible GPU options with pay-as-you-go billing, persistent storage, and customizable resources to match your project requirements.
Configuration Options
Customize your storage configuration before launching your GPU instance.
Container Disk
30–200 GB
Temporary system disk for your instance environment.
Data is erased when the instance restarts. Do not use this disk for files you need to preserve.
Persistent Volume
0–1,000 GB
Persistent storage mounted at /workspace, designed to keep your project files across instance restarts.
Data is retained across restarts. Storage charges continue while the instance is stopped.
Storage note: Set Persistent Volume to 0 GB if no persistent workspace volume is needed. Otherwise, choose a size from 10 GB to 1,000 GB. Persistent storage is billed even while the instance is stopped.
•● Powerful RunPod GPU Hosting for AI and Machine Learning
Accelerate demanding computing tasks with RunPod GPU hosting through GhostVPS, combining high-performance NVIDIA GPUs, configurable resources, persistent storage, and flexible deployment options for AI development and GPU-intensive applications.
Choose from NVIDIA GPUs ranging from RTX and L-series models to data-center accelerators such as A100, H100, H200, and B200, subject to availability. With support for multi-GPU instances, RunPod can power demanding tasks such as LLM fine-tuning, deep learning, and generative AI, depending on the selected GPU and VRAM capacity.
Launch projects faster with templates for PyTorch, TensorFlow, CUDA, and ComfyUI. Jupyter Notebook, SSH access, customizable disk space, and persistent workspace storage help streamline development while keeping project files available across supported instance restarts.
Track GPU utilization, system resources, logs, and hourly costs to monitor workload performance and spending. Pay-per-minute compute billing helps avoid a fixed monthly GPU commitment, while stopped instances can retain storage charges. Public IPv4 access, one-click template reinstallation, and 24/7 support further simplify day-to-day management.
•● FREQUENTLY ASKED QUESTIONS
Find answers to common questions about RunPod GPU hosting, including NVIDIA GPU options, VRAM capacity, AI development environments, persistent storage, SSH access, usage-based billing, instance management, and GPU resource monitoring.
RunPod GPU configurations can include NVIDIA RTX 3090, RTX 4090, RTX 5090, A40, L40S, A100, H100, H200, B200, and other models, subject to availability. The available GPU selection depends on current inventory and the configuration chosen.
RunPod GPU configurations support between 1 and 8 GPUs per instance. Multi-GPU setups can help accommodate demanding AI training, model fine-tuning, and inference workloads, depending on software compatibility and resource requirements.
RunPod offers GPU options with up to 141 GB of VRAM per GPU, depending on the selected model. Check the specifications of your chosen GPU to ensure sufficient memory for your model size, batch size, and computational workload.
Yes. RunPod plans include persistent workspace storage for keeping data in the /workspace directory across supported instance restarts. Container disk and persistent volume sizes can be configured to suit your project, and applicable storage charges may continue when an instance is stopped.
Ready-made templates are available for tools and environments such as PyTorch, TensorFlow, CUDA, and ComfyUI. RunPod also provides password-protected Jupyter Notebook access and SSH connectivity for development, configuration, and remote server administration.
RunPod GPU compute is billed by usage time, with the listed service supporting pay-per-minute billing from your cloud balance rather than a fixed monthly GPU fee. When an instance is stopped, GPU compute charges can cease, while applicable persistent storage charges may remain.
Yes. The service includes live usage monitoring for GPU, VRAM, CPU, RAM, and disk resources, along with logs and hourly cost reports. The listed service supports managing up to 50 GPU instances and provides one-click template reinstallation.
The listed RunPod GPU service offers Tier 3+ secure data center locations in the USA, Canada, and Europe. It also includes 24/7 support and a stated 99.9% uptime guarantee. Actual GPU and location availability depends on the selected configuration and current capacity.
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