lambda-labs-gpu-cloud
Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill lambda-labs --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 ac
- When to use Lambda Labs
- Quick start
- Account setup
- Launch via console
- Connect via SSH
- GPU instances
- Available GPUs
- Instance configurations
- Launch times
- Lambda Stack
- Verify installation
- Python API
- Installation
- Authentication
Get instance IP from console ssh ubuntu@<INSTANCE-IP> Or with specific key ssh -i ~/.ssh/lambda_key ubuntu@<INSTANCE-IP> Included software Check GPU nvidia-smi Check PyTorch python -c "import torch; print(torch.cuda.is_available())" Check CUDA version
What does the lambda-labs-gpu-cloud skill do?
Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill lambda-labs --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.