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Get Your First GPUs in 5 Minutes

This guide will walk you through purchasing GPU time, deploying a workload, and cleaning up.

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Prerequisites

  • Unix-based system (macOS or Linux)
  • Credit card for payment
  • SSH key (optional, but recommended)

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Step 1: Install the CLI

curl -sSL https://galadriel.com/install.sh | sh
Verify installation: 
galadriel version

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Step 2: Configure Authentication

Create an account and get your API token: 
galadriel configure
You’ll be prompted to:
  1. Enter your API token (get one at https://app.galadriel.com/settings/tokens)
  2. Set default zone (e.g., us-west-1)
  3. Add your SSH public key (optional)
Your configuration is saved to ~/.galadriel/config.yaml.
Create an API token at https://app.galadriel.com/settings/tokens with full access permissions.

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Step 3: Check the Marketplace

Before buying, check current prices and availability: 
# View orderbook for H100 GPUs in us-west-1
galadriel orderbook --gpu-type h100 --zone us-west-1
Output: 
📊 Orderbook: H100 @ us-west-1 (2025-11-11 14:00 UTC)

Market Depth:
  Best Bid:  $1.30/GPU/hr  (24 GPUs)
  Best Ask:  $1.40/GPU/hr  (16 GPUs)
  Spread:    $0.10 (7.69%)
  Mid Price: $1.35

Bids (Buy Orders):                     Asks (Sell Orders):
  $1.30 ████████████████ 24 GPUs        $1.40 ████████ 16 GPUs
  $1.25 ███████████ 16 GPUs             $1.45 ████ 8 GPUs
  $1.20 ███████████████████ 32 GPUs     $1.50 ████████████ 24 GPUs

Last Trade: $1.35 (8 GPUs) @ 13:55 UTC
24h Volume: 1,920 GPU-hours
Get a price estimate: 
galadriel prices estimate --gpu-type h100 --gpus 16 --zone us-west-1

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Step 4: Purchase GPUs

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Option A: Market Order (Instant)

Buy GPUs immediately at the current market price: 
galadriel order buy \
  --gpu-type h100 \
  --gpus 16 \
  --duration 24h \
  --zone us-west-1 \
  --type market
Market orders execute instantly but you pay the current best ask price. For this example, you’d pay ~$1.40/GPU/hr.

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Option B: Limit Order (Save Money)

Wait for a specific price: 
galadriel order buy \
  --gpu-type h100 \
  --gpus 16 \
  --duration 24h \
  --price 1.30 \
  --zone us-west-1
Limit orders can save 10-25% but may take minutes to hours to fill. Use this for non-urgent workloads.
Expected output: 
🛒 Creating market buy order for 16 GPUs...
✓ Order matched instantly!
✓ Order ID: ord_abc123
✓ Matched at: $1.40/GPU/hour
✓ Total cost: $537.60 (16 GPUs * 24h * $1.40)
✓ Lease ID: lse_xyz123
✓ Nodes: gpu-node-042, gpu-node-043

📦 Getting kubeconfig...
✓ Saved to ~/.kube/galadriel-lse_xyz123.conf

🎉 Your cluster is ready!

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Step 5: Connect to Your GPUs

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Option A: Using kubectl

Set your kubeconfig: 
export KUBECONFIG=~/.kube/galadriel-lse_xyz123.conf
Verify access: 
kubectl get nodes
Output: 
NAME            STATUS   ROLES    AGE   VERSION
gpu-node-042    Ready    <none>   30s   v1.30.0
gpu-node-043    Ready    <none>   30s   v1.30.0

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Option B: Using SSH

SSH directly to a node: 
galadriel ssh lse_xyz123
Or specify a node: 
galadriel ssh lse_xyz123 --node gpu-node-042

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Step 6: Deploy a Workload

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Simple GPU Test

Create gpu-test.yaml: 
apiVersion: v1
kind: Pod
metadata:
  name: gpu-test
spec:
  containers:
  - name: cuda-test
    image: nvidia/cuda:12.2.0-base-ubuntu22.04
    command: ["nvidia-smi"]
    resources:
      limits:
        nvidia.com/gpu: 1
  restartPolicy: Never
Deploy: 
kubectl apply -f gpu-test.yaml
kubectl logs gpu-test

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Multi-Node Training Job

Create distributed-training.yaml: 
apiVersion: batch/v1
kind: Job
metadata:
  name: distributed-training
spec:
  completions: 2
  parallelism: 2
  template:
    spec:
      containers:
      - name: trainer
        image: nvcr.io/nvidia/pytorch:23.10-py3
        command:
          - python
          - -m
          - torch.distributed.run
          - --nproc_per_node=8
          - --nnodes=2
          - train.py
        resources:
          limits:
            nvidia.com/gpu: 8
        volumeMounts:
        - name: infiniband
          mountPath: /dev/infiniband
      volumes:
      - name: infiniband
        hostPath:
          path: /dev/infiniband
      nodeSelector:
        nvidia.com/gpu.product: NVIDIA-H100-80GB-HBM3
Deploy: 
kubectl apply -f distributed-training.yaml
kubectl get pods -w

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Step 7: Monitor Your Lease

Check lease details: 
galadriel get lse_xyz123
List all active leases: 
galadriel list
Output: 
LEASE ID    GPUs  GPU TYPE  ZONE       STATUS  EXPIRES             COST/HR
lse_xyz123  16    H100      us-west-1  active  2025-11-12 14:00   $1.40

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Step 8: Cleanup (Optional)

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Finished Early?

If you’re done before your 24 hours are up, you have two options:

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Option A: Terminate and Get Refund

galadriel terminate lse_xyz123
You’ll get a prorated refund for unused time.

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Option B: Resell Unused Time

Recover even more by reselling to the marketplace: 
# Resell remaining time at $1.20/GPU/hr
galadriel lease resell lse_xyz123 --price 1.20
If matched, you’ll receive:
  • Original refund from Galadriel
  • Net recovery from marketplace sale (minus 10% platform fee)
See the Reselling Guide for details.

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Automatic Cleanup

Your lease automatically expires and cleans up at the end time. You’ll receive:
  • 5-minute warning notification
  • Automatic namespace deletion
  • Complete resource cleanup

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Next Steps

Understand the Marketplace

Learn about order types and pricing strategies

Cluster Specs

GPU types, system specs, and access model

API Reference

REST API and SDK documentation

Examples

Real-world training examples

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Common Issues

Limit orders may not fill if your price is too low. Check the current orderbook:
galadriel orderbook --gpu-type h100 --zone us-west-1
Cancel and create a new order with a higher price, or switch to a market order.
Make sure your kubeconfig is set:
export KUBECONFIG=~/.kube/galadriel-lse_xyz123.conf
Or get the kubeconfig again:
galadriel kubeconfig lse_xyz123 --save ~/.kube/galadriel.conf
Verify your SSH key was added during configuration:
cat ~/.galadriel/config.yaml | grep ssh_public_key
If not set, reconfigure:
galadriel configure

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Getting Help