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+# How to run gpt-oss with SkyPilot and vLLM
+
+[SkyPilot](https://skypilot.readthedocs.io/) is an open-source system that lets you run AI workloads on any infrastructure, cloud or K8s, with unified execution and high GPU availability. Combined with [vLLM](https://docs.vllm.ai/en/latest/), you can deploy **gpt-oss-20b** or **gpt-oss-120b** models across 16+ cloud providers and on-prem GPU workstations to Kubernetes clusters, with automatic failover and cost optimization.
+
+This guide shows you how to leverage SkyPilot's multi-cloud capabilities to serve gpt-oss models at scale, building on top of the vLLM serving infrastructure covered in [our vLLM guide](https://cookbook.openai.com/articles/gpt-oss/run-vllm).
+
+## Why SkyPilot + vLLM?
+
+Using SkyPilot with vLLM provides several advantages:
+
+- **Multi-cloud deployment**: Run on AWS, GCP, Azure, Lambda Labs, RunPod, and 10+ other clouds
+- **On-premises and Kubernetes support**: Deploy on your own GPU workstations or existing K8s clusters
+- **Reserved GPU optimization**: Efficiently utilize your pre-allocated cloud resources and reserved instances
+- **Automatic GPU provisioning**: SkyPilot finds available GPUs across clouds and on-prem infrastructure
+- **Cost optimization**: Automatically chooses the cheapest cloud/region or uses your existing hardware
+- **Built-in autoscaling**: SkyServe provides production-ready scaling and load balancing across hybrid environments
+- **Spot instance support**: Save up to 70% with automatic failover between spot, on-demand, and on-prem resources
+
+## Prerequisites
+
+Before starting, you'll need:
+
+- **Python 3.11+** installed on your local machine
+- **Cloud credentials** for at least one provider (AWS, GCP, Azure, Lambda Labs, Nebius, etc.)
+- **GPU availability** in your chosen cloud(s)
+
+## Quick Setup
+
+1. **Install SkyPilot**  
+   Install SkyPilot with support for all cloud providers:
+
+```shell
+pip install 'skypilot[all]'
+```
+
+2. **Configure cloud credentials**  
+   SkyPilot will automatically detect credentials from your cloud CLI tools:
+
+```shell
+# Check which clouds are configured
+sky check
+```
+
+3. **Create a SkyPilot configuration**  
+   Create a file named `gpt-oss-vllm.sky.yaml`:
+
+```yaml
+resources:
+  accelerators: {H100:1, H200:1}
+  disk_size: 512 
+  ports: 8000
+
+envs:
+  MODEL_NAME: openai/gpt-oss-20b
+
+setup: |
+  sudo apt-get update
+  sudo apt-get install -y python3-dev build-essential
+  uv venv --python 3.12 --seed
+  source .venv/bin/activate
+  uv pip install --pre vllm==0.10.1+gptoss \
+    --extra-index-url https://wheels.vllm.ai/gpt-oss/ \
+    --extra-index-url https://download.pytorch.org/whl/nightly/cu128 \
+    --index-strategy unsafe-best-match
+  uv pip install hf_xet openai==1.99.1
+  hf download $MODEL_NAME
+
+run: |
+  source .venv/bin/activate
+  vllm serve $MODEL_NAME \
+    --host 0.0.0.0 \
+    --port 8000 \
+    --trust-remote-code \
+
+service:
+  replica_policy:
+    min_replicas: 1
+    max_replicas: 3
+    target_qps_per_replica: 5.0
+    upscale_delay_seconds: 300
+    downscale_delay_seconds: 600
+  readiness_probe: /v1/models
+```
+
+## Deploy to Any Cloud
+
+### Basic deployment
+
+Launch gpt-oss on the cheapest available GPU:
+
+```shell
+# For 20B model
+sky launch -c gpt-oss gpt-oss-vllm.sky.yaml
+# For 120B model 
+sky launch -c gpt-oss gpt-oss-vllm.sky.yaml --env MODEL_NAME=openai/gpt-oss-120b 
+```
+
+SkyPilot will:
+1. Find the cheapest cloud/region with available GPUs (defined in the `resources.accelerators` field)
+2. Provision the instance
+3. Install dependencies
+4. Start the vLLM server
+5. Expose the endpoint
+
+### Get your endpoint
+
+Once deployed, get the API endpoint:
+
+```shell
+ENDPOINT=$(sky status --endpoint 8000 gpt-oss)
+echo "API available at: http://$ENDPOINT"
+```
+
+## Use the deployed model
+
+The deployed model exposes the same OpenAI-compatible API as local vLLM.
+
+Test with cURL:
+
+```shell
+curl http://$ENDPOINT/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "openai/gpt-oss-20b",
+    "messages": [
+      {
+        "role": "system",
+        "content": "You are a helpful assistant."
+      },
+      {
+        "role": "user", 
+        "content": "Explain what MXFP4 quantization is."
+      }
+    ]
+  }' | jq .
+```
+
+Test with Python:
+
+```py
+import os
+from openai import OpenAI
+ 
+ENDPOINT = os.getenv('ENDPOINT')
+client = OpenAI(
+    base_url=f"http://{ENDPOINT}/v1",
+    api_key="EMPTY"
+)
+ 
+result = client.chat.completions.create(
+    model="openai/gpt-oss-20b",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Explain what MXFP4 quantization is."}
+    ]
+)
+ 
+print(result.choices[0].message.content)
+```
+
+## Production deployment with SkyServe
+
+For production workloads, use [**SkyServe**](https://docs.skypilot.co/en/latest/serving/sky-serve.html) for automatic scaling, load balancing, and high availability:
+
+```shell
+sky serve up -n gpt-oss-service gpt-oss-vllm.sky.yaml
+```
+
+This provides:
+- **Automatic scaling**: Scale replicas based on load
+- **Load balancing**: Distribute requests across replicas
+- **Health checks**: Automatic restart on failures
+- **Rolling updates**: Zero-downtime deployments
+
+Monitor your service:
+
+```shell
+sky serve status gpt-oss-service
+```
+
+## Integration with existing tools
+
+The deployed endpoint is OpenAI-compatible, so it works with:
+- [**LangChain**](https://www.langchain.com/): For building complex AI applications
+- [**OpenAI Agents SDK**](https://openai.github.io/openai-agents-python/): For agentic workflows
+- [**llm CLI tool**](https://github.com/simonw/llm): For command-line interactions
+- **Any OpenAI-compatible client**: Drop-in replacement
+
+## Cleanup
+
+When you're done, clean up your resources:
+
+```shell
+# Fully terminate the cluster
+sky down gpt-oss
+
+# For SkyServe deployments
+sky serve down gpt-oss-service
+
+# Check all active resources
+sky status
+```
diff --git a/authors.yaml b/authors.yaml
index 592296d19d..0b51f22b7e 100644
--- a/authors.yaml
+++ b/authors.yaml
@@ -431,3 +431,8 @@ Vaibhavs10:
   name: "vb"
   website: "https://huggingface.co/reach-vb"
   avatar: "https://cdn-avatars.huggingface.co/v1/production/uploads/1655385361868-61b85ce86eb1f2c5e6233736.jpeg"
+
+alex000kim:
+  name: "Alex Kim"
+  website: "https://alex000kim.com"
+  avatar: "https://alex000kim.com/about/profile-pic.png"
diff --git a/registry.yaml b/registry.yaml
index 85f30787c4..e4d6c69594 100644
--- a/registry.yaml
+++ b/registry.yaml
@@ -4,6 +4,17 @@
 # should build pages for, and indicates metadata such as tags, creation date and
 # authors for each page.
 
+- title: How to run gpt-oss with SkyPilot and vLLM
+  path: articles/gpt-oss/run-skypilot-vllm.md
+  date: 2025-08-06
+  authors:
+    - alex000kim
+  tags:
+    - open-models
+    - gpt-oss
+    - deployment
+    - cloud
+
 - title: How to run gpt-oss-20b on Google Colab
   path: articles/gpt-oss/run-colab.ipynb
   date: 2025-08-06