RunPod offers Serverless GPU computing for AI Inference and Training, allowing users to pay by the second for their compute usage. This flexible platform is designed to scale dynamically, meeting the computational needs of AI workloads from the smallest to the largest scales.

Key features of RunPod Serverless GPU include:

  • AI Inference: Handle millions of inference requests daily and can be scaled to handle billions, making it an ideal solution for machine learning inference tasks. This allows users to scale their machine learning inference while keeping costs low.

  • AI Training: Machine learning training tasks that can take up to 12 hours. GPUs can be spun up per request and scaled down once the task is done, providing a flexible solution for AI training needs.

  • Autoscale: Dynamically scale workers from 0 to 100 on the Secure Cloud platform, which is highly available and distributed globally. This provides users with the computational resources exactly when needed.

  • Container Support: Bring any Docker container to RunPod. Both public and private image repositories are supported, allowing users to configure their environment exactly how they want.

  • 3s Cold-Start: To help reduce cold-start times, RunPod proactively pre-warms workers. The total start time will vary based on the runtime, but for stable diffusion, the total start time is 3 seconds cold-start plus 5 seconds runtime.

  • Metrics and Debugging: Transparency is vital in debugging. RunPod provides access to GPU, CPU, Memory, and other metrics to help users understand their computational workloads. Full debugging capabilities for workers through logs and SSH are also available, with a web terminal for even easier access.

  • Webhooks: Users can leverage webhooks to get data output as soon as a request is done. Data is pushed directly to the user's Webhook API, providing instant access to results.

RunPod Serverless GPUs are not just for AI Inference and Training. They're also great for a variety of other use cases. Feel free to use them for tasks like rendering, molecular dynamics, or any other computational task that suits your fancy.