Lightning AI

O que é Lightning AI?

Official access notice (checked October 22, 2026): Access to the Lightning Studio Platform is temporarily suspended during an investigation into an October 2026 security incident. Studios, Deployments, Pipelines, and Drive are affected. GPU cluster rental infrastructure and managed Kubernetes and SLURM services are not affected. Lightning AI says it is working to restore platform access. Check the official security notice for current restoration information.

Lightning AI is a platform for building AI systems, bringing cloud-based development, model training, deployment, and data workflows into one environment. Developers can work in a Studio, train models at scale, and deploy models as APIs. The platform is part of the ecosystem created by the PyTorch Lightning team and is accompanied by open-source libraries for model development, distributed computing, serving, and data processing.

O que Lightning AI pode fazer?

Cloud development Studios

Develop in cloud Studios, with notebook and workspace options documented for building and running AI projects.

Model training

Train and fine-tune models, including multi-node workflows and experiment management.

Model inference and deployment

Deploy models as APIs, with documented options for custom models, containers, batch jobs, pipelines, and vLLM inference.

Data workflows

Manage data for AI projects through uploads, datasets, data connections, cloud folders, artifacts, and data movement.

GPU infrastructure choices

Use GPU cloud resources, clusters, or bring your own cloud, according to the platform documentation.

Related open-source libraries

The ecosystem includes PyTorch Lightning, Fabric, TorchMetrics, Thunder, LitServe, LitData, and LitGPT for model training, scaling, metrics, compilation, serving, data processing, and LLM workflows.

Casos de uso

“Prototype an AI application”

Use a cloud Studio or notebook to develop a model or application and host a web app during development.

“Scale model training”

Move from local or single-machine experiments to multi-node training, with experiment management for tracking work.

“Publish a model endpoint”

Deploy a custom model or container as an API for applications that need inference, or use batch jobs for offline workloads.

“Prepare and move training data”

Organize datasets and artifacts, connect data sources, and move data between supported cloud workflows.

Perguntas frequentes

What is Lightning AI?

It is a platform for building AI that combines cloud development in Studios with training, data workflows, and model deployment.

Can I train and deploy models on the platform?

Yes. The platform documentation covers model pretraining and fine-tuning, multi-node training, and deployment as APIs, containers, or batch jobs.

Can teams use their own cloud infrastructure?

The documentation includes a bring-your-own-cloud option as well as GPU cloud resources and clusters.

Does Lightning AI provide open-source tools?

Yes. Its documented open-source ecosystem includes PyTorch Lightning, Fabric, TorchMetrics, Thunder, LitServe, LitData, and LitGPT.