lambda-labs-gpu-cloud

# Lambda Labs GPU Cloud Comprehensive guide to running ML workloads on Lambda Labs GPU cloud with on-demand instances and 1-Click Clusters. ## When to use Lambda Labs **Use Lambda Labs when:** - Need dedicated GPU instances with full SSH access - Running long training jobs (hours to days) - Want simple pricing with no egress fees - Need persistent storage across sessions - Require high-performance multi-node clusters (16-512 GPUs) - Want pre-installed ML stack (Lambda Stack with PyTorch, CUDA, NCCL) **Key features:** - **GPU variety**: B200, H100, GH200, A100, A10, A6000, V100 - **Lambda Stack**: Pre-installed PyTorch, TensorFlow, CUDA, cuDNN, NCCL - **Persistent filesystems**: Keep data across instance restarts - **1-Click Clusters**: 16-512 GPU Slurm clusters with InfiniBand - **Simple pricing**: Pay-per-minute, no egress fees - **Global regions**: 12+ regions worldwide **Use alternatives instead:** - **Modal**: For serverless, auto-scaling workloads - **SkyPilot**: For multi-cloud orchestration and cost optimization - **RunPod**: For cheaper spot instances and serverless endpoints - **Vast.ai**: For GPU marketplace with lowest prices ## Quick start ### Account setup 1. Create account at https://lambda.ai 2. Add payment method 3. Generate API key from dashboard 4. Add SSH key (required before launching instances) ### Launch via console 1. Go to https://cloud.lambda.ai/instances 2. Click "Launch instance" 3. Select GPU type and region 4. Choose SSH key 5. Optionall...