lambda-labs-gpu-cloud

Launch on-demand GPU clusters by using 1-Click Clusters to provision 16-512 GPUs with InfiniBand for distributed ML training. You can select your topology and region, then connect via SSH to start workloads immediately.

Yes, you can attach persistent file systems to your GPU instances to keep ML datasets and checkpoints across restarts. This prevents data loss when scaling instances up or down on demand.

No, you do not need to manually install PyTorch, CUDA, or cuDNN. Lambda Stack is pre-installed on the GPU instances, providing the necessary ML software stack out of the box.

Available GPU types for ML inference and training workloads include H100, A100, and A6000. You can choose your preferred instance topology and region through the Lambda Cloud API or centralized console.

Automate provisioning for multi-GPU instances by accessing the Lambda Cloud API. This allows automated orchestration of your ML training and inference workloads, including 1-Click Clusters and persistent filesystem management.

The best way to scale GPU infrastructure without maintaining hardware is using on-demand cloud instances with pay-as-you-go billing. This eliminates hardware ownership costs while providing scalable multi-GPU clusters.