lambda-labs-gpu-cloud

Files (3)

• SKILL.md
• advanced-usage.md · references/
• troubleshooting.md · references/

SKILL.md (first lines)

# 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

### Installation

From Orchestra-Research/AI-Research-SKILLs, MIT.

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