Lepton

Use Case 1: Multi-Cloud GPU Scaling and Availability

Problem: Developers often face "GPU poverty" or supply shortages on their primary cloud provider (e.g., AWS or Azure), which stalls model training or deployment. Moving to a different provider usually requires re-architecting the infrastructure stack, managing new credentials, and changing deployment scripts.
Solution: NVIDIA DGX Cloud Lepton unifies global GPU supply into a single platform. It decouples the AI platform from the underlying infrastructure, allowing developers to access GPUs from various providers and regions through one consistent interface without rewriting their code.
Example: An AI startup training a large language model finds that H100 instances are unavailable in their current region. Using Lepton, they instantly pivot their training job to a partner GPU marketplace in another region that has capacity, maintaining the exact same workflow and environment settings.

Use Case 2: Rapid Prototyping with Serverless NVIDIA NIMs

Problem: Setting up an optimized inference environment for a new model—handling dependencies, GPU drivers, and scaling logic—can take days of engineering effort just to test a single feature.
Solution: Lepton provides instant access to serverless endpoints and prebuilt NVIDIA NIM (NVIDIA Inference Microservices). This allows developers to move from a prototype to a functional API call in minutes.
Example: A software engineer wants to add a "Smart Summarization" feature to a productivity app. Instead of configuring a dedicated GPU server, they use Lepton to access a serverless Llama-3 NIM endpoint. Once the feature is validated with users, they use the same Lepton platform to scale that deployment to dedicated GPU resources for production.

Use Case 3: Compliant "Sovereign AI" for Regulated Industries

Problem: Companies in healthcare, finance, or government sectors often have strict data sovereignty requirements. They cannot send sensitive data to a GPU cluster in another country, but their local region may lack the advanced AI compute needed for training.
Solution: Lepton allows users to "run where your data lives" by connecting to a vast network of local Cloud Partners (NCPs) and specific regional providers. This ensures compute happens within the required jurisdictional boundaries.
Example: A German hospital group wants to train a diagnostic AI on sensitive patient scans. They use Lepton to identify and deploy their training containers on a specialized NVIDIA-certified cloud provider located physically within Germany, ensuring compliance with GDPR and local data privacy laws.

Use Case 4: Unified Workflow from Local Dev to Global Production

Problem: AI teams often struggle with "environment drift," where a model works perfectly on a developer's local workstation but fails when moved to a massive DGX cluster or a multi-node cloud environment due to library mismatches or hardware differences.
Solution: DGX Cloud Lepton creates a unified experience across development, training, and inference. It provides a consistent compute environment so that the transition from a local prototype to a global production scale is frictionless.
Example: A data science team develops a computer vision model on their local machines. When they are ready to scale, they push the workload to Lepton. The platform automatically handles the orchestration to run the same code across a multi-node Blackwell architecture cluster in the cloud, ensuring identical performance and behavior.