/lambda-labs

name: lambda-labs-gpu-cloud
description: 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.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Infrastructure, GPU Cloud, Training, Inference, Lambda Labs]
dependencies: [lambda-cloud-client>=1.0.0]

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. Optionally attach filesystem 6. Launch and wait 3-15 minutes

Connect via SSH

# Get instance IP from console
ssh ubuntu@<INSTANCE-IP>

# Or with specific key
ssh -i ~/.ssh/lambda_key ubuntu@<INSTANCE-IP>

GPU instances

Available GPUs

| GPU | VRAM | Price/GPU/hr | Best For | |-----|------|--------------|----------| | B200 SXM6 | 180 GB | $4.99 | Largest models, fastest training | | H100 SXM | 80 GB | $2.99-3.29 | Large model training | | H100 PCIe | 80 GB | $2.49 | Cost-effective H100 | | GH200 | 96 GB | $1.49 | Single-GPU large models | | A100 80GB | 80 GB | $1.79 | Production training | | A100 40GB | 40 GB | $1.29 | Standard training | | A10 | 24 GB | $0.75 | Inference, fine-tuning | | A6000 | 48 GB | $0.80 | Good VRAM/price ratio | | V100 | 16 GB | $0.55 | Budget training |

Instance configurations

8x GPU: Best for distributed training (DDP, FSDP)
4x GPU: Large models, multi-GPU training
2x GPU: Medium workloads
1x GPU: Fine-tuning, inference, development

Launch times

• Single-GPU: 3-5 minutes
• Multi-GPU: 10-15 minutes

Lambda Stack

All instances come with Lambda Stack pre-installed:

# Included software
- Ubuntu 22.04 LTS
- NVIDIA drivers (latest)
- CUDA 12.x
- cuDNN 8.x
- NCCL (for multi-GPU)
- PyTorch (latest)
- TensorFlow (latest)
- JAX
- JupyterLab

Verify installation

# Check GPU
nvidia-smi

# Check PyTorch
python -c "import torch; print(torch.cuda.is_available())"

# Check CUDA version
nvcc --version

Python API

Installation

pip install lambda-cloud-client

Authentication

import os
import lambda_cloud_client

# Configure with API key
configuration = lambda_cloud_client.Configuration(
    host="https://cloud.lambdalabs.com/api/v1",
    access_token=os.environ["LAMBDA_API_KEY"]
)

List available instances

with lambda_cloud_client.ApiClient(configuration) as api_client:
    api = lambda_cloud_client.DefaultApi(api_client)

    # Get available instance types
    types = api.instance_types()
    for name, info in types.data.items():
        print(f"{name}: {info.instance_type.description}")

Launch instance

from lambda_cloud_client.models import LaunchInstanceRequest

request = LaunchInstanceRequest(
    region_name="us-west-1",
    instance_type_name="gpu_1x_h100_sxm5",
    ssh_key_names=["my-ssh-key"],
    file_system_names=["my-filesystem"],  # Optional
    name="training-job"
)

response = api.launch_instance(request)
instance_id = response.data.instance_ids[0]
print(f"Launched: {instance_id}")

List running instances

instances = api.list_instances()
for instance in instances.data:
    print(f"{instance.name}: {instance.ip} ({instance.status})")

Terminate instance

from lambda_cloud_client.models import TerminateInstanceRequest

request = TerminateInstanceRequest(
    instance_ids=[instance_id]
)
api.terminate_instance(request)

SSH key management

from lambda_cloud_client.models import AddSshKeyRequest

# Add SSH key
request = AddSshKeyRequest(
    name="my-key",
    public_key="ssh-rsa AAAA..."
)
api.add_ssh_key(request)

# List keys
keys = api.list_ssh_keys()

# Delete key
api.delete_ssh_key(key_id)

CLI with curl

List instance types

curl -u $LAMBDA_API_KEY: \
  https://cloud.lambdalabs.com/api/v1/instance-types | jq

Launch instance

curl -u $LAMBDA_API_KEY: \
  -X POST https://cloud.lambdalabs.com/api/v1/instance-operations/launch \
  -H "Content-Type: application/json" \
  -d '{
    "region_name": "us-west-1",
    "instance_type_name": "gpu_1x_h100_sxm5",
    "ssh_key_names": ["my-key"]
  }' | jq

Terminate instance

curl -u $LAMBDA_API_KEY: \
  -X POST https://cloud.lambdalabs.com/api/v1/instance-operations/terminate \
  -H "Content-Type: application/json" \
  -d '{"instance_ids": ["<INSTANCE-ID>"]}' | jq

Persistent storage

Filesystems

Filesystems persist data across instance restarts:

# Mount