Lightning AI

Lightning AI is a cloud-based platform designed to accelerate the entire lifecycle of artificial intelligence development, from prototyping to production. Built by the creators of PyTorch Lightning, it provides developers and AI teams with a unified environment where they can code, train, scale, and deploy models directly from a browser without any local setup or infrastructure management. The platform offers several key components tailored to different stages of AI workflows. AI Studio is a collaborative GPU cloud workspace that integrates AI assistance for debugging, training, and inference. Developers can write code, run experiments, and iterate in real time while an embedded AI copilot helps with code suggestions, error fixing, and performance tuning. This reduces the friction of managing dependencies, environments, and hardware. AI notebooks provide persistent GPU-backed Jupyter-like environments where users can analyze datasets, train models, and visualize results. These notebooks are designed to persist state across sessions, allowing long-running experiments without interruption. For teams that need more computational power, GPU clusters are available for managed training and inference. The platform supports SLURM, Kubernetes, and its own multi-cloud Lightning Execution Cluster (LEC), enabling users to orchestrate distributed training jobs across multiple nodes. This makes it suitable for large-scale transformer models or fine-tuning foundation architectures. Inference is handled through either pay-per-token APIs or custom model serving. Users can deploy their own models as REST endpoints, or let Lightning AI manage the entire serving stack, including scaling and load balancing. The platform also includes templates for building multimodal agents. For example, it