coreweave-core-workflow-a
Deploy KServe InferenceService on CoreWeave with autoscaling and GPU scheduling. Use when serving ML models with KServe, configuring scale-to-zero, or deploying production inference endpoints on CoreWeave. Trigger with phrases like "coreweave inference service", "coreweave kserve", "coreweave model serving", "deploy model on coreweave".
Best use case
coreweave-core-workflow-a is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Deploy KServe InferenceService on CoreWeave with autoscaling and GPU scheduling. Use when serving ML models with KServe, configuring scale-to-zero, or deploying production inference endpoints on CoreWeave. Trigger with phrases like "coreweave inference service", "coreweave kserve", "coreweave model serving", "deploy model on coreweave".
Teams using coreweave-core-workflow-a should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/coreweave-core-workflow-a/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How coreweave-core-workflow-a Compares
| Feature / Agent | coreweave-core-workflow-a | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Deploy KServe InferenceService on CoreWeave with autoscaling and GPU scheduling. Use when serving ML models with KServe, configuring scale-to-zero, or deploying production inference endpoints on CoreWeave. Trigger with phrases like "coreweave inference service", "coreweave kserve", "coreweave model serving", "deploy model on coreweave".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# CoreWeave Core Workflow: KServe Inference
## Overview
Deploy production inference services on CoreWeave using KServe InferenceService with GPU scheduling, autoscaling, and scale-to-zero. CKS natively integrates with KServe for serverless GPU inference.
## Prerequisites
- Completed `coreweave-install-auth` setup
- KServe available on your CKS cluster
- Model stored in S3, GCS, or HuggingFace
## Instructions
### Step 1: Deploy an InferenceService
```yaml
# inference-service.yaml
apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
name: llama-inference
annotations:
autoscaling.knative.dev/class: "kpa.autoscaling.knative.dev"
autoscaling.knative.dev/metric: "concurrency"
autoscaling.knative.dev/target: "1"
autoscaling.knative.dev/minScale: "1"
autoscaling.knative.dev/maxScale: "5"
spec:
predictor:
minReplicas: 1
maxReplicas: 5
containers:
- name: kserve-container
image: vllm/vllm-openai:latest
args:
- "--model"
- "meta-llama/Llama-3.1-8B-Instruct"
- "--port"
- "8080"
ports:
- containerPort: 8080
protocol: TCP
resources:
limits:
nvidia.com/gpu: "1"
memory: 48Gi
cpu: "8"
requests:
nvidia.com/gpu: "1"
memory: 32Gi
cpu: "4"
env:
- name: HUGGING_FACE_HUB_TOKEN
valueFrom:
secretKeyRef:
name: hf-token
key: token
affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: gpu.nvidia.com/class
operator: In
values: ["A100_PCIE_80GB"]
```
```bash
kubectl apply -f inference-service.yaml
kubectl get inferenceservice llama-inference -w
```
### Step 2: Scale-to-Zero Configuration
```yaml
# For dev/staging -- scale down to zero when idle
metadata:
annotations:
autoscaling.knative.dev/minScale: "0" # Scale to zero
autoscaling.knative.dev/maxScale: "3"
autoscaling.knative.dev/scaleDownDelay: "5m"
```
### Step 3: Test the Endpoint
```bash
# Get inference URL
INFERENCE_URL=$(kubectl get inferenceservice llama-inference \
-o jsonpath='{.status.url}')
curl -X POST "${INFERENCE_URL}/v1/chat/completions" \
-H "Content-Type: application/json" \
-d '{"model": "meta-llama/Llama-3.1-8B-Instruct", "messages": [{"role": "user", "content": "Hello!"}]}'
```
## Error Handling
| Error | Cause | Solution |
|-------|-------|----------|
| InferenceService not ready | GPU not available | Check node capacity and affinity |
| Scale-to-zero cold start | First request after idle | Set `minScale: 1` for production |
| Model loading timeout | Large model download | Pre-cache model in PVC |
| OOMKilled | Model too large | Use multi-GPU or quantized model |
## Resources
- [CoreWeave Inference](https://docs.coreweave.com/docs/products/cks/tutorials/deploy-vllm-inference)
- [KServe Documentation](https://kserve.github.io/website/)
## Next Steps
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