Lambda Labs
Lambda Labs provides on-demand GPU cloud infrastructure specifically designed for deep learning. Unlike general cloud providers (AWS, GCP), Lambda focuses exclusively on AI workloads with pre-configured environments, PyTorch/TensorFlow ready, and simple pricing. Rent NVIDIA H100 ($1.99/hr), A100 40GB/80GB ($1.10-$1.29/hr), or A6000 ($0.50/hr) instantly. Key advantages: (1) No complex setup—SSH and start training, (2) Persistent storage included, (3) JupyterLab pre-installed, (4) Often 30-50% cheaper than AWS/GCP for equivalent GPUs. Popular with researchers, startups, and ML engineers who need GPUs without DevOps overhead.
Overview
Lambda Labs specializes in GPU cloud for AI practitioners. Spin up H100 instance in 60 seconds, SSH in, start training—no complex VPC setup, no security group configuration, no AMI selection. Pre-installed: NVIDIA drivers, CUDA, cuDNN, PyTorch, TensorFlow, JupyterLab. Persistent storage: attach volumes, data survives instance termination. Pricing: simple per-hour rates, no hidden costs. Use cases: model training (fine-tune Llama 3, train diffusion models), research experiments (iterate quickly without hardware investment), inference serving (deploy models on A6000s).
Available GPUs
- **H100 PCIe**: $1.99/hr—80GB HBM3, 3TB/s bandwidth, best for LLM training
- **A100 80GB**: $1.29/hr—80GB HBM2e, excellent for large models
- **A100 40GB**: $1.10/hr—40GB HBM2e, cost-effective for most workloads
- **A6000**: $0.50/hr—48GB GDDR6, great for inference and smaller training
- **RTX 6000 Ada**: $0.75/hr—48GB GDDR6, balanced performance
- **Multi-GPU Instances**: 2x, 4x, 8x GPU configurations available
Business Integration
Lambda Labs enables AI development without capital expenditure. Startups train custom models without buying $30K GPUs. Research teams run experiments on H100s ($1.99/hr) instead of waiting months for on-premise hardware. ML engineers fine-tune models on A100s for client projects, bill hourly GPU costs directly. The key advantage: pay only for compute used—train model in 8 hours ($15 on A100), terminate instance. No multi-year cloud commits, no unused reserved instances.
Getting Started
Technical Specifications
- **Instant Provisioning**: Launch in 60 seconds (when available)
- **Pre-configured**: Ubuntu 22.04, CUDA 12.x, PyTorch, TensorFlow, JupyterLab
- **Networking**: 10 Gbps network, SSH access, JupyterLab on port 8888
- **Storage**: Local NVMe + optional persistent volumes
- **Pricing**: Per-hour, no minimum commit, terminate anytime
- **Support**: Email support, community Slack channel
Best Practices
- Use persistent volumes for datasets—local NVMe erased on termination
- Terminate instances when not training—billed by the hour
- Use screen or tmux for long training runs—survives SSH disconnection
- Monitor GPU utilization with nvidia-smi—ensure you're using GPU
- Save checkpoints frequently—instances can occasionally be preempted
- Use A100 40GB for most workloads—best price/performance