name: coreweave-deploy-integration description: 'Deploy inference services on CoreWeave with Helm charts and Kustomize.

Use when deploying multi-model inference, managing GPU deployments at scale,

or templating CoreWeave manifests.

Trigger with phrases like "deploy coreweave", "coreweave helm",

"coreweave kustomize", "coreweave deployment patterns".

' allowed-tools: Read, Write, Edit, Bash(helm:), Bash(kubectl:), Bash(kustomize:*) version: 1.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io tags:

• saas
• gpu-cloud
• kubernetes
• inference
• coreweave compatibility: Designed for Claude Code

CoreWeave Deploy Integration

Overview

Deploy GPU-accelerated inference services on CoreWeave Kubernetes (CKS). This skill covers containerizing inference workloads with NVIDIA CUDA base images, configuring GPU resource limits and node affinity for A100/H100 scheduling, setting up health checks that validate GPU availability and model loading, and executing rolling updates that respect GPU node draining. CoreWeave's scheduler requires explicit GPU resource requests to place pods on the correct hardware tier.

Docker Configuration

FROM nvidia/cuda:12.4.0-runtime-ubuntu22.04 AS base
RUN apt-get update && apt-get install -y --no-install-recommends \
    python3 python3-pip curl && rm -rf /var/lib/apt/lists/*
WORKDIR /app
COPY requirements.txt ./
RUN pip3 install --no-cache-dir -r requirements.txt

FROM base
RUN groupadd -r app && useradd -r -g app app
COPY --chown=app:app src/ ./src/
COPY --chown=app:app models/ ./models/
USER app
EXPOSE 8080
HEALTHCHECK --interval=30s --timeout=10s --retries=3 \
  CMD curl -f http://localhost:8080/health || exit 1
CMD ["python3", "src/server.py"]

Environment Variables

export COREWEAVE_API_KEY="cw_xxxxxxxxxxxx"
export COREWEAVE_NAMESPACE="tenant-my-org"
export MODEL_NAME="meta-llama/Llama-3.1-8B-Instruct"
export GPU_TYPE="A100_PCIE_80GB"
export GPU_COUNT="1"
export LOG_LEVEL="info"
export PORT="8080"

Health Check Endpoint

import express from 'express';
import { execSync } from 'child_process';

const app = express();

app.get('/health', async (req, res) => {
  try {
    const gpuInfo = execSync('nvidia-smi --query-gpu=name,memory.used --format=csv,noheader').toString().trim();
    const modelLoaded = globalThis.modelReady === true;
    if (!modelLoaded) throw new Error('Model not loaded');
    res.json({ status: 'healthy', gpu: gpuInfo, model: process.env.MODEL_NAME, timestamp: new Date().toISOString() });
  } catch (error) {
    res.status(503).json({ status: 'unhealthy', error: (error as Error).message });
  }
});

Deployment Steps

Step 1: Build

docker build -t registry.coreweave.com/my-org/inference-svc:latest .
docker push registry.coreweave.com/my-org/inference-svc:latest

Step 2: Run

# k8s/deployment.yaml
resources:
  limits:
    nvidia.com/gpu: 1
    cpu: "4"
    memory: "48Gi"
nodeSelector:
  gpu.nvidia.com/class: A100_PCIE_80GB

kubectl apply -f k8s/deployment.yaml -n tenant-my-org

Step 3: Verify

kubectl get pods -n tenant-my-org -l app=inference-svc
curl -s http://inference-svc.tenant-my-org.svc.cluster.local:8080/health | jq .

Step 4: Rolling Update

kubectl set image deployment/inference-svc \
  inference=registry.coreweave.com/my-org/inference-svc:v2 \
  -n tenant-my-org
kubectl rollout status deployment/inference-svc -n tenant-my-org --timeout=600s

Error Handling

Resources

• CoreWeave CKS Docs
• CoreWeave GPU Types

Next Steps

See coreweave-webhooks-events.