CtrlK
BlogDocsLog inGet started
Tessl Logo

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.

61

Quality

73%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Medium

Suggest reviewing before use

Fix and improve this skill with Tessl

tessl review fix ./09-infrastructure/lambda-labs/SKILL.md
SKILL.md
Quality
Evals
Security

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

GPUVRAMPrice/GPU/hrBest For
B200 SXM6180 GB$4.99Largest models, fastest training
H100 SXM80 GB$2.99-3.29Large model training
H100 PCIe80 GB$2.49Cost-effective H100
GH20096 GB$1.49Single-GPU large models
A100 80GB80 GB$1.79Production training
A100 40GB40 GB$1.29Standard training
A1024 GB$0.75Inference, fine-tuning
A600048 GB$0.80Good VRAM/price ratio
V10016 GB$0.55Budget 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 location
/lambda/nfs/<FILESYSTEM_NAME>

# Example: save checkpoints
python train.py --checkpoint-dir /lambda/nfs/my-storage/checkpoints

Create filesystem

  1. Go to Storage in Lambda console
  2. Click "Create filesystem"
  3. Select region (must match instance region)
  4. Name and create

Attach to instance

Filesystems must be attached at instance launch time:

  • Via console: Select filesystem when launching
  • Via API: Include file_system_names in launch request

Best practices

# Store on filesystem (persists)
/lambda/nfs/storage/
  ├── datasets/
  ├── checkpoints/
  ├── models/
  └── outputs/

# Local SSD (faster, ephemeral)
/home/ubuntu/
  └── working/  # Temporary files

SSH configuration

Add SSH key

# Generate key locally
ssh-keygen -t ed25519 -f ~/.ssh/lambda_key

# Add public key to Lambda console
# Or via API

Multiple keys

# On instance, add more keys
echo 'ssh-rsa AAAA...' >> ~/.ssh/authorized_keys

Import from GitHub

# On instance
ssh-import-id gh:username

SSH tunneling

# Forward Jupyter
ssh -L 8888:localhost:8888 ubuntu@<IP>

# Forward TensorBoard
ssh -L 6006:localhost:6006 ubuntu@<IP>

# Multiple ports
ssh -L 8888:localhost:8888 -L 6006:localhost:6006 ubuntu@<IP>

JupyterLab

Launch from console

  1. Go to Instances page
  2. Click "Launch" in Cloud IDE column
  3. JupyterLab opens in browser

Manual access

# On instance
jupyter lab --ip=0.0.0.0 --port=8888

# From local machine with tunnel
ssh -L 8888:localhost:8888 ubuntu@<IP>
# Open http://localhost:8888

Training workflows

Single-GPU training

# SSH to instance
ssh ubuntu@<IP>

# Clone repo
git clone https://github.com/user/project
cd project

# Install dependencies
pip install -r requirements.txt

# Train
python train.py --epochs 100 --checkpoint-dir /lambda/nfs/storage/checkpoints

Multi-GPU training (single node)

# train_ddp.py
import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP

def main():
    dist.init_process_group("nccl")
    rank = dist.get_rank()
    device = rank % torch.cuda.device_count()

    model = MyModel().to(device)
    model = DDP(model, device_ids=[device])

    # Training loop...

if __name__ == "__main__":
    main()
# Launch with torchrun (8 GPUs)
torchrun --nproc_per_node=8 train_ddp.py

Checkpoint to filesystem

import os

checkpoint_dir = "/lambda/nfs/my-storage/checkpoints"
os.makedirs(checkpoint_dir, exist_ok=True)

# Save checkpoint
torch.save({
    'epoch': epoch,
    'model_state_dict': model.state_dict(),
    'optimizer_state_dict': optimizer.state_dict(),
    'loss': loss,
}, f"{checkpoint_dir}/checkpoint_{epoch}.pt")

1-Click Clusters

Overview

High-performance Slurm clusters with:

  • 16-512 NVIDIA H100 or B200 GPUs
  • NVIDIA Quantum-2 400 Gb/s InfiniBand
  • GPUDirect RDMA at 3200 Gb/s
  • Pre-installed distributed ML stack

Included software

  • Ubuntu 22.04 LTS + Lambda Stack
  • NCCL, Open MPI
  • PyTorch with DDP and FSDP
  • TensorFlow
  • OFED drivers

Storage

  • 24 TB NVMe per compute node (ephemeral)
  • Lambda filesystems for persistent data

Multi-node training

# On Slurm cluster
srun --nodes=4 --ntasks-per-node=8 --gpus-per-node=8 \
  torchrun --nnodes=4 --nproc_per_node=8 \
  --rdzv_backend=c10d --rdzv_endpoint=$MASTER_ADDR:29500 \
  train.py

Networking

Bandwidth

  • Inter-instance (same region): up to 200 Gbps
  • Internet outbound: 20 Gbps max

Firewall

  • Default: Only port 22 (SSH) open
  • Configure additional ports in Lambda console
  • ICMP traffic allowed by default

Private IPs

# Find private IP
ip addr show | grep 'inet '

Common workflows

Workflow 1: Fine-tuning LLM

# 1. Launch 8x H100 instance with filesystem

# 2. SSH and setup
ssh ubuntu@<IP>
pip install transformers accelerate peft

# 3. Download model to filesystem
python -c "
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf')
model.save_pretrained('/lambda/nfs/storage/models/llama-2-7b')
"

# 4. Fine-tune with checkpoints on filesystem
accelerate launch --num_processes 8 train.py \
  --model_path /lambda/nfs/storage/models/llama-2-7b \
  --output_dir /lambda/nfs/storage/outputs \
  --checkpoint_dir /lambda/nfs/storage/checkpoints

Workflow 2: Batch inference

# 1. Launch A10 instance (cost-effective for inference)

# 2. Run inference
python inference.py \
  --model /lambda/nfs/storage/models/fine-tuned \
  --input /lambda/nfs/storage/data/inputs.jsonl \
  --output /lambda/nfs/storage/data/outputs.jsonl

Cost optimization

Choose right GPU

TaskRecommended GPU
LLM fine-tuning (7B)A100 40GB
LLM fine-tuning (70B)8x H100
InferenceA10, A6000
DevelopmentV100, A10
Maximum performanceB200

Reduce costs

  1. Use filesystems: Avoid re-downloading data
  2. Checkpoint frequently: Resume interrupted training
  3. Right-size: Don't over-provision GPUs
  4. Terminate idle: No auto-stop, manually terminate

Monitor usage

  • Dashboard shows real-time GPU utilization
  • API for programmatic monitoring

Common issues

IssueSolution
Instance won't launchCheck region availability, try different GPU
SSH connection refusedWait for instance to initialize (3-15 min)
Data lost after terminateUse persistent filesystems
Slow data transferUse filesystem in same region
GPU not detectedReboot instance, check drivers

References

  • Advanced Usage - Multi-node training, API automation
  • Troubleshooting - Common issues and solutions

Resources

Repository
Orchestra-Research/AI-Research-SKILLs
Path

09-infrastructure/lambda-labs/SKILL.md

Commit

773a529

Last updated
First committed

Also appears in

NousResearch/hermes-agent
In sync

since Jul 23, 2026

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.

Claim skill