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lambda-labs

Launch and manage Lambda Labs GPU cloud instances for ML training and inference.

Updated Aug 22, 2026
Search Tags:#cloud infrastructure#distributed training#LLM fine-tuning#H100#ML training#lambda labs#gpu cloud
One-click install
npx skills add https://github.com/vivekgoquest/hermes-agent-stable --skill lambda-labs-vivekgoquest
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: lambda-labs
Source: https://github.com/vivekgoquest/hermes-agent-stable/tree/main/optional-skills/mlops/lambda-labs
Command: npx skills add https://github.com/vivekgoquest/hermes-agent-stable --skill lambda-labs-vivekgoquest

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires lambda-cloud-client, and includes references (resource) components.

What problem does it solve? Provisioning dedicated GPU infrastructure for ML training is complex and time-consuming. This Skill guides you through launching, configuring, and managing Lambda Labs GPU cloud instances so you can run training jobs, fine-tune models, and serve inference without managing physical hardware. ## Core Features & Use Cases - Instance Lifecycle Management: Launch, monitor, and terminate GPU instances (B200, H100, A100, A10, and more) via the console, Python API, or curl-based CLI. - Distributed Training Workflows: Run multi-GPU DDP/FSDP training on single nodes or scale to 16-512 GPU 1-Click Slurm clusters with InfiniBand. - Persistent Storage: Attach Lambda filesystems to keep datasets, checkpoints, and models across instance restarts. - Use Case: Fine-tune a 70B LLM by launching an 8x H100 instance with an attached filesystem, running accelerate-based training with checkpoints saved to persistent storage, then terminating the instance when done. ## Quick Start Use the lambda-labs skill to launch a single H100 instance in us-west-1 with my SSH key and show me how to connect to it.

Frequently Asked Questions about lambda-labs

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

FAQPage Schema
How do I launch a Lambda Labs GPU instance with the Python API?▼

Install lambda-cloud-client, configure it with your API key, then call launch_instance with a region, instance type name, and SSH key names. The instance typically becomes active in 3-15 minutes depending on GPU count.

Which Lambda Labs GPU should I use for fine-tuning an LLM?▼

For 7B models, a single A100 40GB works well; for 70B models, use an 8x H100 instance. For inference or development, A10 or A6000 instances offer lower hourly costs.

Lambda Labs vs RunPod vs Vast.ai for GPU training?▼

Lambda Labs provides dedicated instances with full SSH access, pre-installed Lambda Stack, and no egress fees. RunPod offers cheaper spot and serverless options, while Vast.ai is a marketplace with the lowest prices but less consistency.

Does Lambda Labs keep my data after terminating an instance?▼

No, the root volume is ephemeral and data is lost on termination. Attach a persistent Lambda filesystem at launch time and store datasets, checkpoints, and models under /lambda/nfs/<filesystem_name>.

Why is my SSH connection to a Lambda instance refused?▼

Connection refused usually means the instance is still initializing; single-GPU instances take 3-5 minutes and multi-GPU up to 15 minutes. Also verify the instance status is active and your SSH key was added before launch.

How do I fix CUDA out of memory errors during training?▼

Reduce batch size, enable gradient checkpointing, use mixed precision with autocast, or clear the CUDA cache with torch.cuda.empty_cache(). If the model still does not fit, switch to a larger GPU such as A100 80GB or H100.

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