GPU Cloud

Onboarding Guide for GPU Cloud: Instantaneous Containerized GPU Instances

GPU Cloud services by Podwide offer a rapid deployment solution for containerized GPU instances, supporting elastic billing and second-level deployment. Our platform provides a wealth of resources, including public base images like Stable Diffusion, PyTorch, Miniconda3, and popular recommended images like Lama Cleaner, ComfyUI, and Paraformer for speech recognition.


Podwide’s GPU Cloud services provide dedicated instances equipped with powerful GPUs, offering a robust and scalable AI infrastructure. These instances are optimized for AI workloads, enabling seamless deployment and operation of AI models. With pre-configured environments tailored for deep learning, users can quickly start their AI projects without the hassle of manual setup.

Service Features

Rapid Deployment

  • Instant Availability: Deploy dedicated NVIDIA RTX 4090 or H100 GPU instances within seconds, equipped with the necessary Jupyter Notebook environment.

  • Pre-configured Environments: Instances come with TensorFlow, PyTorch, Keras, and NVIDIA Cuda drivers pre-installed, saving you from the complex setup process.

Enhanced Efficiency

  • Optimized Performance: Our GPU instances are optimized for both training and inference, ensuring high performance and efficiency for your AI workloads.

  • Scalable Infrastructure: Easily scale your resources up or down based on project requirements, providing flexibility and cost-efficiency.

Global Distribution

  • Low Latency: Deploy instances in multiple global locations, ensuring minimal latency and optimal model performance.

  • Widespread Accessibility: Benefit from globally distributed resources that enhance your AI project’s reach and effectiveness.

Community Image Sharing and Rewards

  • Community Sharing: Podwide platform provides a feature for sharing community images, allowing users to access and use images created by others.

  • Incentives for Creators: Quality image creators are rewarded with points that can be used to consume computing power, encouraging the spirit of open-source model sharing.

User Benefits

  • Rapid Familiarization: Quickly get started with model development, training, and inference using pre-configured tools and environments.

  • On-Demand Services: Access GPU resources as needed, ensuring cost-efficiency and flexibility for your projects.

  • Cost-Effective: Take advantage of elastic billing, paying only for the resources you use, reducing overall costs.

  • Instant Deployment: Deploy GPU instances in seconds, minimizing setup time and enabling immediate project initiation.

  • Enhanced AI Efficiency: Benefit from optimized environments that streamline AI model training and inference processes.

  • Increased Productivity: Utilize pre-configured tools and environments to focus on your core tasks, boosting overall productivity.

  • Scalability and Flexibility: Scale resources as needed, ensuring your AI infrastructure can grow with your project demands.

  • Low Latency: Enjoy the benefits of low latency and optimal performance by deploying instances globally.

  • Open Source Encouragement: Contribute to and benefit from a community of shared images, fostering collaboration and innovation.

Podwide’s GPU Cloud services provide a powerful, efficient, and flexible solution for AI model development, training, and inference, helping researchers and developers quickly adapt and excel in the AI-driven landscape.

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