lambda-labs-gpu-cloud

You can provision on-demand GPU cloud instances for ML training by launching them directly from the Lambda Labs console and connecting via SSH. This provides simple, immediate access to high-performance hardware without maintenance overhead.

Available GPU cloud architectures for ML training and inference include H100, A100, A10, and B200. You can select your desired GPU type across multiple Lambda regions to optimize for latency and hardware requirements.

Yes, you can scale multi-node training using 1-Click Clusters. This feature simplifies launching large-scale distributed training jobs across connected nodes without extensive manual setup.

No, you do not need to manually install PyTorch or TensorFlow. Lambda Labs instances come with Lambda Stack pre-installed, providing immediate access to PyTorch, TensorFlow, and related ML tools out of the box.

You can preserve ML training checkpoints and datasets by attaching persistent filesystems to your GPU cloud instances. This storage remains available across instance restarts, ensuring your data is retained.

Yes, you can launch GPU cloud resources using either the API or CLI. This allows you to programmatically provision instances, select regions, and integrate resource management into automated ML workflows.