runpod · danstotts-ops · Sep 2, 2026 · Sep 2, 2026
diff --git a/api-reference/overview.mdx b/api-reference/overview.mdx
@@ -1,10 +1,10 @@
 ---
 title: "Overview"
 sidebarTitle: "Overview"
 description: "Use the Runpod API to programmatically manage your compute resources. Review authentication, request formats, and response details for the Runpod API."
 ---

 The Runpod REST API v1 provides programmatic access to all Runpod compute resources. Integrate GPU infrastructure into your applications, workflows, and automation systems.

 <Warning>
 REST API v1 is deprecated and will be retired on **November 15, 2026**. Migrate your integrations to REST API v2 before that date. See the [migration guide](/api-reference-v2/migrate-from-v1) to get started. To migrate automatically, ask your coding agent to run `/runpod:migrate rest` once you've installed the [Runpod skills plugin](/get-started/agent-skills#migrate-an-existing-integration).
@@ -27,6 +27,8 @@
 
 Retrieve the complete OpenAPI specification for client generation, request validation, or tooling integration.
 
+See the [interactive API documentation endpoint reference](/api-reference/docs/GET/docs) to open the browser-based reference directly.
+
 <CodeGroup>
 
 ```bash cURL

diff --git a/community-solutions/copyparty-file-manager/overview.mdx b/community-solutions/copyparty-file-manager/overview.mdx
@@ -1,16 +1,16 @@
 ---
 title: CopyParty file manager
 description: "Web-based GUI for easy file browsing, uploading, downloading, and media viewing on Runpod. Review setup and usage guidance for this Runpod community solution."
 icon: "party-horn"
 ---
 
-# Setting up CopyParty on Runpod
+## Set up CopyParty on Runpod
 
 CopyParty provides a webbased GUI that makes file management simple on Runpod instances. With its intuitive interface, you can browse directories, upload/download files, preview images and videos, and manage your Pod's filesystem without complex command-line operations.
 
 ## About CopyParty

 CopyParty is an open-source project created that provides a portable file server with a web UI. It's perfect for managing files on cloud GPU instances where traditional file transfer methods might be cumbersome.

 For a video demonstration, you can watch the [creator's YouTube tutorial](https://www.youtube.com/watch?v=15_-hgsX2V0).

@@ -33,22 +33,22 @@

 You can access the terminal in two ways:

 #### Option 1: Web terminal
 If you see this option when clicking "Connect" on your Pod page, you have web terminal access:
 ![Web Terminal Access](/community-solutions/copyparty-file-manager/webterminal.png)

 #### Option 2: JupyterLab terminal
 If you have JupyterLab installed on your Pod, you can access the terminal there:
 ![JupyterLab Terminal](/community-solutions/copyparty-file-manager/labsterminal.png)

 ## Installation steps

 ### Step 1: Access your Pod settings

 Navigate to your Pod page and locate the settings:
 ![Edit Pod Settings](/community-solutions/copyparty-file-manager/edit.png)

 ### Step 2: Add an HTTP port

 <Warning>
 **Adding a new port will restart your Pod and erase any data not stored in `/workspace`**
@@ -56,16 +56,16 @@
 Before proceeding, ensure all important files are saved in `/workspace` or backed up elsewhere. Any installed libraries or files outside of `/workspace` will be lost.
 </Warning>

 Add a dedicated HTTP port for the CopyParty interface. If port 8888 is already in use (common for Jupyter), try port 8000 or another available port.

 ![Add HTTP Port](/community-solutions/copyparty-file-manager/addhttp.png)

 ### Step 3: Install and run CopyParty

 Open your terminal (web terminal or Jupyter terminal) and run one of the following commands:

 #### Option 1: Standard installation
 Run CopyParty directly (the session will end if you close the terminal):

 ```bash
 curl -LsSf https://astral.sh/uv/install.sh | sh && source $HOME/.local/bin/env && uv tool run copyparty -p 8000 --allow-csrf
@@ -90,7 +90,7 @@
 </Info>

 This command:
 1. Installs tmux (a terminal multiplexer)
 2. Creates a new tmux session named "copyparty"
 3. Runs CopyParty in the background
 4. Attaches you to the session to see the output
@@ -102,12 +102,12 @@
 - To stop CopyParty: Reattach and press `Ctrl+C`
 </Tip>

 ### Step 4: Access the CopyParty interface

 Once CopyParty is running, click on the port number in your Runpod dashboard:
 ![Access Port](/community-solutions/copyparty-file-manager/port.png)

 ## Using CopyParty

 ### File navigation

@@ -122,7 +122,7 @@
 ### Downloading files

 To download files:
 1. Click on files to select them (they'll be highlighted in pink)
 2. Use the buttons in the bottom right:
   - **"dl"** - Download individual files
   - **"zip"** - Download multiple files as a zip archive
@@ -132,20 +132,20 @@
 ## Tips and best practices

 1. **Data Persistence**: Always store important files in `/workspace` to survive pod restarts
 2. **Port Selection**: Choose a port that doesn't conflict with other services (avoid 8888 if using Jupyter)
 3. **Large Files**: CopyParty handles large file transfers well, making it ideal for model weights and datasets

 ## Troubleshooting

 ### Session ends when terminal closes
 Use the tmux option (Option 2) to keep CopyParty running in the background

 ## Alternative file transfer methods

 While CopyParty provides an excellent web-based solution, Runpod also supports:
 - Direct SSH/SCP transfers (if SSH is enabled)
 - JupyterLab file browser
 - [Runpod CLI](/runpodctl/overview) tool
 - [Cloud storage integration](/pods/storage/cloud-sync) (S3, Google Drive, etc.)

 Choose the method that best fits your workflow and security requirements.
diff --git a/community-solutions/overview.mdx b/community-solutions/overview.mdx
@@ -1,16 +1,16 @@
 ---
-title: "Introduction"
+title: "Community solutions"
 description: "Community-created tools and solutions by and for Runpod users. Review setup and usage guidance for this Runpod community solution."
 icon: "flask"
 ---

 This section showcases innovative tools and solutions created by the Runpod community. These are tools and workflows developed by users who encountered common challenges and built creative solutions to share with others.

 While these tools and solutions are not officially maintained by Runpod, we highlight them here because they demonstrate the ingenuity of our community and can be valuable resources for users facing similar challenges.

 ## Using community solutions

 Community tools and solutions are provided as-is and maintained by their creators. While we showcase them for their utility, please note that you should test in a development environment first, review the source code when available, report issues to the tool creators, and ultimately use them at your own discretion.

 ## Featured tools & solutions

@@ -50,8 +50,8 @@
  </Card>
 </CardGroup>

 Browse through our community tools section in the sidebar for detailed documentation on each tool's purpose, installation, and usage.

 ## Contributing

 Built something useful on Runpod? The community would love to learn about it! Share your tools and get feedback in the [Runpod Discord](https://discord.com/invite/runpod).
diff --git a/get-started/connect-to-runpod.mdx b/get-started/connect-to-runpod.mdx
@@ -1,40 +1,46 @@
 ---
 title: "Choose a workflow"
 description: "Review available methods for accessing and managing Runpod resources. Review setup steps and core concepts for building on Runpod."
 ---

 import { PodsTooltip, EndpointTooltip, ServerlessTooltip } from "/snippets/tooltips.jsx";
 
 Runpod offers multiple ways to access and manage your compute resources. Choose the method that best fits your workflow:
 
+If you are deciding between Pods, Serverless, Public Endpoints, and Clusters, start with the [Runpod product overview](/get-started/products).
+
 ## Runpod console
 
 The Runpod console provides an intuitive web interface to manage <PodsTooltip /> and <EndpointTooltip />s, access Pod terminals, send endpoint requests, monitor resource usage, and view billing and usage history.

 [Launch the Runpod console →](https://www.console.runpod.io)

 ## Connect directly to Pods

 You can connect directly to your running Pods and execute code on them using a variety of methods, including a built-in web terminal, an SSH connection from your local machine, a JupyterLab instance, or a remote VSCode/Cursor development environment.

 [Learn more about Pod connection options →](/pods/connect-to-a-pod)

 ## REST API

 The Runpod REST API allows you to programmatically manage and control compute resources. Use the API to manage Pod lifecycles and <ServerlessTooltip /> endpoints, monitor resource utilization, and integrate Runpod into your applications.

 [Explore the API reference →](/api-reference/docs/GET/openapi-json)

 ## SDKs

 Runpod provides SDKs in Python, JavaScript, Go, and GraphQL to help you integrate Runpod services into your applications.

 [Explore the SDKs →](/serverless/sdks)

 ## Command-line interface (CLI)

 The Runpod CLI allows you to manage Pods from your terminal, execute code on Pods, transfer data between Runpod and local systems, and programmatically manage Serverless endpoints.

 Every Pod comes pre-installed with the `runpodctl` command and includes a Pod-scoped API key for seamless command-line management.
 
 [Learn more about runpodctl →](/runpodctl/overview)
+
+## Coding agents
+
+Use the [agent onboarding guide](/agent-setup) to install Runpod skills and connect the Runpod MCP server to a supported coding agent.
diff --git a/instant-clusters.mdx b/instant-clusters.mdx
@@ -1,4 +1,4 @@
 ---
 title: "Overview"
 sidebarTitle: "Overview"
 description: "Fully managed compute clusters for multi-node training and AI inference. Review configuration and operations guidance for Runpod Clusters."
@@ -28,11 +28,14 @@
   <Card title="Axolotl fine-tuning" href="/instant-clusters/axolotl" icon="screwdriver" horizontal>
     Fine-tune LLMs across multiple GPUs.
   </Card>
+  <Card title="Distributed inference with Ray and vLLM" href="/instant-clusters/ray-vllm" icon="network-wired" horizontal>
+    Serve models across multiple nodes with Ray and vLLM.
+  </Card>
 </CardGroup>
 
 ## How it works

 Runpod provisions multiple GPU nodes in the same <DataCenterTooltip /> connected with high-speed networking. One node is designated primary (`NODE_RANK=0`), and all nodes receive pre-configured environment variables for distributed communication.

 <div style={{ marginLeft: '4rem'}}>
 ```mermaid
@@ -71,24 +74,24 @@
 ```
 </div>

 The high-speed interfaces (`ens1`-`ens8`) handle inter-node communication for <PyTorchTooltip />, <TensorFlowTooltip />, and <SlurmTooltip />. The `eth0` interface on the primary node handles external traffic. See the [configuration reference](/instant-clusters/configuration) for environment variables and network details.

 ## Supported hardware

 | GPU | Network speed | Nodes |
 | --- | ------------- | ----- |
 | B200 | 3200 Gbps | 2-8 nodes (16-64 GPUs) |
 | H200 | 3200 Gbps | 2-8 nodes (16-64 GPUs) |
 | H100 | 3200 Gbps | 2-8 nodes (16-64 GPUs) |
 | A100 | 1600 Gbps | 2-8 nodes (16-64 GPUs) |

 For clusters larger than 8 nodes (up to 512 GPUs), [contact our sales team](https://ecykq.share.hsforms.com/2MZdZATC3Rb62Dgci7knjbA).

 ## Pricing

 Pricing is based on GPU type and number of nodes. See [Instant Clusters pricing](https://www.runpod.io/pricing) for current rates.

 Custom pricing is available for enterprise workloads. [Contact our sales team](https://ecykq.share.hsforms.com/2MZdZATC3Rb62Dgci7knjbA) for details.

 <Note>
 All accounts have a default spending limit. To deploy larger clusters, contact [help@runpod.io](mailto:help@runpod.io).

diff --git a/instant-clusters/axolotl.mdx b/instant-clusters/axolotl.mdx
@@ -1,26 +1,26 @@
 ---
 title: "Deploy an Instant Cluster with Axolotl"
 sidebarTitle: "Axolotl"
 description: "Deploy a Runpod Cluster with Axolotl and configure multi-GPU distributed training for fine-tuning large language models."
 ---
 import { TrainingTooltip, InferenceTooltip } from "/snippets/tooltips.jsx";

 This tutorial demonstrates how to use Instant Clusters with [Axolotl](https://axolotl.ai/) to fine-tune large language models (LLMs) across multiple GPUs. By leveraging PyTorch's distributed <TrainingTooltip /> capabilities and Runpod's high-speed networking infrastructure, you can significantly accelerate your training process compared to single-GPU setups.

 Follow the steps below to deploy a cluster and start training your models efficiently.

 ## Step 1: Deploy an Instant Cluster

 1. Open the [Instant Clusters page](https://www.console.runpod.io/cluster) on the Runpod web interface.
 2. Click **Create Cluster**.
 3. Use the UI to name and configure your Cluster. For this walkthrough, keep **Pod Count** at **2** and select the option for **16x H100 SXM** GPUs. Keep the **Pod Template** at its default setting (Runpod PyTorch).
 4. Click **Deploy Cluster**. You should be redirected to the Instant Clusters page after a few seconds.

 ## Step 2: Set up Axolotl on each Pod

 1. Click your cluster to expand the list of Pods.

 2. Click on a Pod, for example `CLUSTERNAME-pod-0`, to expand the Pod.

 3. Click **Connect**, then click **Web Terminal**.

@@ -51,7 +51,7 @@

 Repeat these steps for **each Pod** in your cluster.

 ## Step 3: Start the training process on each Pod

 Run this command in the web terminal of **each Pod**:

@@ -92,7 +92,7 @@
 Congratulations! You've successfully trained a model using Axolotl on an Instant Cluster. Your fine-tuned model has been saved to the `./outputs/lora-out` directory. You can now use this model for <InferenceTooltip /> or continue training with different parameters.
 </Check>

 ## Step 4: Clean up

 If you no longer need your cluster, make sure you return to the [Instant Clusters page](https://www.console.runpod.io/cluster) and delete your cluster to avoid incurring extra charges.

@@ -106,8 +106,9 @@
 
 Now that you've successfully deployed and tested an Axolotl distributed training job on an Instant Cluster, you can:
 
+* Review the [fine-tuning guide](/fine-tune) for a complete Axolotl workflow and deployment path.
 * **Fine-tune your own models** by modifying the configuration files in Axolotl to suit your specific requirements.
 * **Scale your training** by adjusting the number of Pods in your cluster (and the size of their containers and volumes) to handle larger models or datasets.
 * **Try different optimization techniques** such as DeepSpeed, FSDP (Fully Sharded Data Parallel), or other distributed training strategies.

 For more information on fine-tuning with Axolotl, refer to the [Axolotl documentation](https://github.com/axolotl-ai-cloud/axolotl).
diff --git a/serverless/development/local-testing.mdx b/serverless/development/local-testing.mdx
@@ -1,13 +1,13 @@
 ---
 title: "Local testing"
 description: "Test your Serverless handlers locally before deploying to production. Review configuration and operations guidance for Runpod Serverless."
 ---
 
-Testing your handler locally before deploying saves time and helps you catch issues early. The Runpod SDK provides multiple ways to test your handler function without consuming cloud resources.
+Testing your handler locally before deploying saves time and helps you catch issues early. The Runpod SDK provides multiple ways to test your handler function without consuming cloud resources. See the [Serverless development overview](/serverless/development/overview) for the complete development lifecycle.
 
 ## Basic testing
 
 The simplest way to test your handler is by running it directly with test input.

 ### Inline JSON

@@ -45,7 +45,7 @@

 ## Local API server

 For more comprehensive testing, start a local API server that simulates your Serverless endpoint. This lets you send HTTP requests to test your handler as if it were deployed.

 Start the local server:

@@ -97,7 +97,7 @@

 ### Handling concurrency in your code

 If your handler uses shared state (like global variables), use proper synchronization to avoid race conditions:

 ```python
 import runpod
@@ -149,7 +149,7 @@

 ### Port

 Set a custom port (default is 8000):

 ```sh
 python handler.py --rp_serve_api --rp_api_port 8080
@@ -157,7 +157,7 @@

 ### Host

 Set the hostname (default is "localhost"):

 ```sh
 python handler.py --rp_serve_api --rp_api_host 0.0.0.0
@@ -177,8 +177,8 @@
 | `--rp_api_port` | Sets the server port | 8000 | `--rp_api_port 8080` |
 | `--rp_api_host` | Sets the server hostname | "localhost" | `--rp_api_host 0.0.0.0` |
 | `--rp_api_concurrency` | Sets concurrent workers | 1 | `--rp_api_concurrency 4` |
 | `--rp_log_level` | Controls log verbosity | INFO | `--rp_log_level DEBUG` |
 | `--rp_debugger` | Enables the debugger | Disabled | `--rp_debugger` |
 | `--test_input` | Provides test input as JSON | N/A | `--test_input '{"input": {}}'` |

 ## Combined example
@@ -197,4 +197,4 @@
 - Starts the local API server on port 8080.
 - Uses 4 concurrent workers.
 - Sets the log level to `DEBUG` for maximum information.
-- Enables the debugger for troubleshooting.
+- Enables the debugger for troubleshooting.
diff --git a/serverless/vllm/openai-compatibility.mdx b/serverless/vllm/openai-compatibility.mdx
@@ -1,4 +1,4 @@
 ---
 title: "OpenAI API compatibility"
 sidebarTitle: "OpenAI compatibility"
 description: "Use OpenAI client libraries with your vLLM workers. Review setup, configuration, deployment, and operations guidance for Runpod Serverless."
@@ -39,8 +39,8 @@

 | Endpoint | Description |
 |----------|-------------|
 | `/chat/completions` | Chat model completions (instruction-tuned models) |
 | `/completions` | Text completions (base models) |
 | `/models` | List available models |

 ## Chat completions
@@ -119,7 +119,7 @@
 ## Model name

 The `model` parameter must match either:
 - The Hugging Face model you deployed (e.g., `mistralai/Mistral-7B-Instruct-v0.2`)
 - A custom name set via the `OPENAI_SERVED_MODEL_NAME_OVERRIDE` environment variable

 List available models:
@@ -131,7 +131,7 @@

 ## Parameters

 Standard OpenAI parameters are supported. Include them directly in your request.

 <Accordion title="Common parameters">
 | Parameter | Type | Default | Description |
@@ -169,7 +169,7 @@

 | Variable | Default | Description |
 | --- | --- | --- |
 | `RAW_OPENAI_OUTPUT` | `1` | Enable raw OpenAI SSE format for streaming. |
 | `OPENAI_SERVED_MODEL_NAME_OVERRIDE` | None | Override model name in responses. |
 | `OPENAI_RESPONSE_ROLE` | `assistant` | Role for chat completion responses. |

@@ -190,3 +190,7 @@
 | Authentication error | Use your Runpod API key, not an OpenAI key. |
 | Timeout errors | Increase client timeout for large models. |
 | Unexpected response format | Set `RAW_OPENAI_OUTPUT=1`. |
+
+## Integrate with workflow tools
+
+Follow the [n8n integration guide](/integrations/n8n-integration) to connect an OpenAI-compatible Runpod endpoint to an automated workflow.
diff --git a/tutorials/introduction/overview.mdx b/tutorials/introduction/overview.mdx
@@ -1,4 +1,4 @@
 ---
 title: "Overview"
 sidebarTitle: "Overview"
 description: "Step-by-step guides for building and deploying AI/ML applications on Runpod. Follow the setup and implementation steps in this Runpod tutorial."
@@ -27,6 +27,9 @@
   <Card title="Generate images with ComfyUI" href="/tutorials/serverless/comfyui" icon="images" horizontal>
     Deploy ComfyUI and generate images using JSON workflows.
   </Card>
+  <Card title="Run Ollama on Serverless CPU" href="/tutorials/serverless/run-ollama-inference" icon="microchip" horizontal>
+    Deploy an Ollama server on CPU workers and send an inference request.
+  </Card>
 </CardGroup>
 
 ## Flash
@@ -60,10 +63,21 @@
  </Card>
 </CardGroup>

 ## Public Endpoints

 <CardGroup cols={3}>
  <Card title="Build a text-to-video pipeline" href="/tutorials/public-endpoints/text-to-video-pipeline" icon="video" horizontal>
     Chain multiple Public Endpoints to generate videos from text.
   </Card>
 </CardGroup>
+
+## More resources
+
+<CardGroup cols={2}>
+  <Card title="Container tutorials" href="/containers" icon="container-storage" horizontal>
+    Learn container fundamentals, Dockerfile creation, persistence, and common Docker commands.
+  </Card>
+  <Card title="Community video resources" href="/references/video-resources" icon="circle-play" horizontal>
+    Browse community tutorials about Runpod workflows and supporting Linux tools.
+  </Card>
+</CardGroup>