lambda-labs-gpu-cloud

To provision on-demand GPU cloud instances for ML training, launch an instance from the Lambda Cloud console and connect directly via SSH using your configured key pair for immediate workload execution.

Yes, you can run multi-node distributed training using 1-Click Clusters to deploy scalable ML pipelines across dedicated GPU workloads without managing the underlying hardware infrastructure.

The GPU cloud features Lambda Stack pre-installed, providing common ML frameworks out of the box to accelerate environment setup and eliminate manual dependency configuration before training.

Persistent storage is supported for scalable ML workloads, ensuring your datasets and training checkpoints remain intact across instance restarts without needing manual data migration.

The best way to access dedicated GPU instances for inference is through SSH access with key pair management, providing direct terminal control over your reserved cloud resources.

Region-based GPU capacity applies to on-demand instances, meaning available GPU types and cluster scales may vary by geographical region and require checking current capacity before deployment.