Lambda Labs Gpu Cloud

---
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.
license: MIT
metadata:
  version: 1.0.0
  category: ml-inference-and-ops
  maintainer: Kalaris Labs
  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

```bash
# 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:

```bash
# 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

```bash
# Check GPU
nvidia-smi

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

# Check CUDA version
nvcc --version
```

## Python API

Details, code examples and parameter tables: [references/python-api.md](references/python-api.md). Read it when this step applies.

## CLI with curl

### List instance types

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

### Launch instance

```bash
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

```bash
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:

```bash
# 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

```bash
# 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

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

# Add public key to Lambda console
# Or via API
```

### Multiple keys

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

### Import from GitHub

```bash
# On instance
ssh-import-id gh:username
```

### SSH tunneling

```bash
# 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

```bash
# 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

```bash
# 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)

```python
# 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()
```

```bash
# Launch with torchrun (8 GPUs)
torchrun --nproc_per_node=8 train_ddp.py
```

### Checkpoint to filesystem

```python
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

```bash
# 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

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

## Common workflows

### Workflow 1: Fine-tuning LLM

```bash
# 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

```bash
# 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

| Task | Recommended GPU |
|------|-----------------|
| LLM fine-tuning (7B) | A100 40GB |
| LLM fine-tuning (70B) | 8x H100 |
| Inference | A10, A6000 |
| Development | V100, A10 |
| Maximum performance | B200 |

### 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

| Issue | Solution |
|-------|----------|
| Instance won't launch | Check region availability, try different GPU |
| SSH connection refused | Wait for instance to initialize (3-15 min) |
| Data lost after terminate | Use persistent filesystems |
| Slow data transfer | Use filesystem in same region |
| GPU not detected | Reboot instance, check drivers |

## References

- **[Advanced Usage](references/advanced-usage.md)** - Multi-node training, API automation
- **[Troubleshooting](references/troubleshooting.md)** - Common issues and solutions

## Resources

- **Documentation**: https://docs.lambda.ai
- **Console**: https://cloud.lambda.ai
- **Pricing**: https://lambda.ai/instances
- **Support**: https://support.lambdalabs.com
- **Blog**: https://lambda.ai/blog

## Related skills

- `modal-serverless-gpu`: Serverless GPU cloud platform for running ML workloads.
- `skypilot-multi-cloud-orchestration`: Multi-cloud orchestration for ML workloads with automatic cost optimization.
- `modal`: Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs.