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lambda-labs-gpu-cloud

Launch and manage on-demand Lambda Labs GPU cloud instances via API.

228|35|Updated Mar 9, 2026
Search Tags:#cloud#gpu#training#inference#ml#clusters
One-click install
npx skills add https://github.com/kaminocorp/hermes-alpha --skill lambda-labs-gpu-cloud-kaminocorp
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: lambda-labs-gpu-cloud
Source: https://github.com/kaminocorp/hermes-alpha/tree/main/hermes-agent/skills/mlops/cloud/lambda-labs
Command: npx skills add https://github.com/kaminocorp/hermes-alpha --skill lambda-labs-gpu-cloud-kaminocorp

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Lambda Labs GPU Cloud provides on-demand, high-performance GPU compute with persistent storage, enabling ML training and inference without bespoke infrastructure.

Core Features & Use Cases

  • GPU variety: B200 SXM6, H100 SXM, GH200, A100 80GB, A10, A6000, V100
  • Lambda Stack: Pre-installed ML stack (PyTorch, CUDA, etc.)
  • Persistent filesystems: Attach storage across sessions
  • 1-Click Clusters: 16-512 GPU Slurm clusters with InfiniBand
  • Simple pricing: Pay-per-minute with no egress fees
  • Global regions: 12+ regions worldwide
  • Use cases: Large-scale training, distributed training, and scalable inference across single or multi-node setups

Quick Start

Create a Lambda Labs account, add a payment method, generate an API key, then launch a GPU instance in the Lambda cloud console and connect via SSH.

Frequently Asked Questions about lambda-labs-gpu-cloud

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I launch on-demand GPU instances for ML training?▼

To launch GPU instances for ML training, create a Lambda Labs account, add a payment method, generate an API key, then provision your compute via the cloud console or REST API and connect through SSH.

Can I run distributed training across multi-node GPU clusters?▼

Yes, you can run distributed training across multi-node GPU clusters using the 1-Click Clusters feature, which provisions 16 to 512 GPUs in a Slurm cluster with InfiniBand networking.

What GPU varieties are available for scalable inference and fine-tuning?▼

Available GPUs for inference and fine-tuning include B200 SXM6, H100 SXM, GH200, A100 80GB, A10, A6000, and V100, providing flexible compute options across various performance tiers.

Do I need to manually install PyTorch and CUDA on cloud GPU instances?▼

No, you do not need to manually install PyTorch and CUDA because the instances include Lambda Stack, a pre-installed ML software stack ready for immediate model training and inference.

Does persistent storage work across different GPU cloud sessions?▼

Persistent filesystems can be attached across multiple sessions, allowing you to retain data and model artifacts storage without needing to transfer files between transient compute instances.

What are the pricing constraints for on-demand GPU cloud compute?▼

Pricing for GPU cloud compute follows a pay-per-minute model with no egress fees, meaning you only pay for the exact duration your ML training or inference instance is actively running.

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