Agent skill · marketing growth · nousresearch
lambda-labs
On-demand GPU cloud instances for ML training.
Why this skill is useful
Provides specific commands and configurations for launching and managing GPU instances on Lambda Labs that the AI wouldn't reliably generate on its own.
What it needs
Requires lambda-cloud-client installed locally. Requires lambda.ai account access. About 6k tokens when loaded. Last updated 2026-08-07. 226,679 stars on the source repository.
What this skill does
Lambda Labs GPU Cloud 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. Optionally attach filesystem 6. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nousresearch/lambda-labs