| name | coreweave-hello-world |
| description | Deploy a GPU workload on CoreWeave with kubectl.
Use when running your first GPU job, testing inference,
or verifying CoreWeave cluster access.
Trigger with phrases like "coreweave hello world", "coreweave first deploy",
"coreweave gpu test", "run on coreweave".
|
| allowed-tools | Read, Write, Edit, Bash(kubectl:*) |
| version | 1.11.0 |
| license | MIT |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
| tags | ["saas","gpu-cloud","kubernetes","inference","coreweave"] |
| compatibility | Designed for Claude Code |
CoreWeave Hello World
Community-contributed. Not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc.
Overview
Deploy your first GPU workload on CoreWeave: a simple inference service using vLLM or a batch CUDA job. CoreWeave runs Kubernetes on bare-metal GPU nodes with A100, H100, and L40 GPUs.
Prerequisites
- Completed
coreweave-install-auth setup
- kubectl configured with CoreWeave kubeconfig
- Namespace with GPU quota
Instructions
Step 1: Deploy a vLLM Inference Server
apiVersion: apps/v1
kind: Deployment
metadata:
name: vllm-server
spec:
replicas: 1
selector:
matchLabels:
app: vllm-server
template:
metadata:
labels:
app: vllm-server
spec:
containers:
- name: vllm
image: vllm/vllm-openai:latest
args:
- "--model"
- "meta-llama/Llama-3.1-8B-Instruct"
- "--port"
- "8000"
ports:
- containerPort:
[]