Fix broken links: CLAUDE.md references to .claude/*.md

CLAUDE.md

@@ -1,21 +1,21 @@
 # CLAUDE.md
 
 This file provides guidance to Claude Code when working with this repository.
 
 ## Project Overview
 
 This is the Runpod documentation site, built with [Mintlify](https://mintlify.com/). The documentation covers Runpod's cloud GPU platform: Serverless endpoints, Pods, Flash SDK, storage, and APIs.
 
 ## Quick Reference
 
 | Topic | File |
 |-------|------|
-| Directory structure, navigation, snippets, tooltips | [.claude/architecture.md](.claude/architecture.md) |
-| Writing style, capitalization, terminology | [.claude/style-guide.md](.claude/style-guide.md) |
-| Running and writing documentation tests | [.claude/testing.md](.claude/testing.md) |
-| Local dev, linting, publishing workflow | [.claude/development.md](.claude/development.md) |
+| Directory structure, navigation, snippets, tooltips | `.claude/architecture.md` |
+| Writing style, capitalization, terminology | `.claude/style-guide.md` |
+| Running and writing documentation tests | `.claude/testing.md` |
+| Local dev, linting, publishing workflow | `.claude/development.md` |
 
 ## Key Commands
 
 ```bash
 mintlify dev                    # Start local dev server
@@ -27,17 +27,17 @@
 
 **Claude should continuously learn and improve these docs.**
 
 If you discover something that would be useful for future sessions, ask me:
 > "I noticed [insight]. Would you like me to add this to `.claude/[appropriate-file].md`?"
 
 Examples of things worth capturing:
 - Patterns that work well (or don't) in this codebase
 - Common mistakes to avoid
 - Useful commands or workflows discovered during tasks
 - Clarifications about how Runpod products work
 
 ## Terminology Quick Reference
 
 **Capitalize:** Runpod, Pods, Serverless, Hub, Instant Clusters, Flash, Secure Cloud, Community Cloud, Public Endpoint
 
 **Lowercase:** endpoint, worker, template, handler, network volume, data center, cluster, fine-tune, repo

instant-clusters/ray-vllm.mdx

@@ -1,13 +1,13 @@
 ---
 title: "Deploy an Instant Cluster with Ray and vLLM"
 sidebarTitle: "Ray + vLLM"
 description: "Run distributed inference across multiple nodes using Ray and vLLM on an Instant Cluster."
 tag: BETA
 ---
 
 This tutorial shows how to use Instant Clusters with Ray to run distributed inference on large language models. By combining Ray's cluster management with vLLM's tensor and pipeline parallelism, you can serve models that exceed the memory of a single node — for example, a 70B parameter model across multiple 8×H100 pods.
 
 Ray handles the cluster topology; vLLM uses it to split the model across GPUs both within each node (tensor parallelism) and across nodes (pipeline parallelism).
 
 <Note>
   Distributed inference with Ray and vLLM on Instant Clusters is currently in beta. Join our [Discord](https://discord.gg/runpod) to provide feedback and get support.
@@ -22,23 +22,23 @@
 
 ---
 
 ## Step 1: Deploy an Instant Cluster
 
 1. Open the [Instant Clusters page](https://console.runpod.io/instant-clusters).
 2. Click **Create Cluster**.
 3. Name your cluster and configure it. For this walkthrough, set **Pod Count** to **2** and select **8× H100 SXM GPUs** per pod. Use the **Runpod PyTorch** template as your base image.
 
    <Note>
    Increase `/dev/shm` when configuring your pod. The default (64 MB) is too small for large tensor-parallel workloads. Set it to at least 8 GB. In the pod configuration, add the environment variable `MALLOC_ARENA_MAX=1` and set `--shm-size` to `8g` in your Docker run options.
    </Note>
 
 4. Click **Deploy Cluster**. You are redirected to the Instant Clusters page.
 
 ---
 
 ## Step 2: Start the Ray head on pod-0
 
 The first pod (`CLUSTERNAME-pod-0`) runs the Ray head node. All other pods connect to it as workers.
 
 1. Click your cluster to expand the pod list.
 2. Click **CLUSTERNAME-pod-0**, then click **Connect → Web Terminal**.
@@ -55,7 +55,7 @@
    bash ray-vllm-cluster/head.sh
    ```
 
    The script sets the correct NIC address and starts Ray:
 
    ```bash
    # head.sh (excerpt — see full script in the repo)
@@ -76,7 +76,7 @@
 
 ## Step 3: Join the worker pods to the cluster
 
 Repeat this for each remaining pod in the cluster (`pod-1`, `pod-2`, …).
 
 1. In the Instant Clusters page, click the next pod and open its **Web Terminal**.
 2. Clone the same scripts:
@@ -111,7 +111,7 @@
      --num-gpus=$NUM_TRAINERS
    ```
 
    `$MASTER_ADDR` is injected automatically by Runpod into all pods in the cluster — it resolves to `pod-0`.
 
 ---
 
@@ -137,9 +137,9 @@
 
 ---
 
 ## Step 5: Launch distributed inference with vLLM
 
 Run this on `pod-0` only. vLLM uses the Ray cluster that is already running.
 
 ```bash
 bash ray-vllm-cluster/serve.sh
@@ -167,7 +167,7 @@
 
 ## Step 6: Test the endpoint
 
 Once vLLM reports that it is ready, validate from `pod-0`:
 
 ```bash
 # Check the server is healthy
@@ -190,9 +190,9 @@
 
 ---
 
 ## Step 7: Clean up
 
 When you are done, return to the [Instant Clusters page](https://console.runpod.io/instant-clusters) and delete your cluster. Leaving it running continues to incur charges.
 
 ---
 
@@ -213,22 +213,23 @@
 ## Common issues
 
 **Ray workers don't join**
 Confirm `$MASTER_ADDR` resolves from each worker pod. Run `ping $MASTER_ADDR` in a worker terminal. If it fails, the cluster network may still be initializing — wait 30 seconds and try again.
 
 **vLLM OOM during model load**
 Check that `/dev/shm` is large enough (at least 8 GB for 70B models). Also verify that `--tensor-parallel-size` matches the number of GPUs per node — a mismatch causes uneven shard sizes.
 
 **`VLLM_HOST_IP` binding error**
 This error occurs when vLLM tries to bind to `0.0.0.0` on a pod with multiple network interfaces. Make sure `VLLM_HOST_IP` is set to the internal IP (`hostname -I | awk '{print $1}'`) before starting the server.
 
 **Stale Ray cluster after restart**
 If you restart a pod, Ray does not automatically rejoin the cluster. Rerun `head.sh` on `pod-0` first, then `worker.sh` on all other pods.
 
 ---
 
 ## Next steps
 
-- Adapt the serve script to load your own model from a [GlobalStore](/storage/globalstore) or [Network Volume](/storage/network-volumes) mount.
+{/* TODO: The link target /storage/globalstore does not exist. Restore the link once a GlobalStore docs page is published, or replace "GlobalStore" with the correct product name. */}
+- Adapt the serve script to load your own model from a GlobalStore or [Network Volume](/storage/network-volumes) mount.
 - Scale up by increasing the pod count and adjusting `--pipeline-parallel-size` accordingly.
 - Try [Axolotl on an Instant Cluster](/instant-clusters/axolotl) for distributed fine-tuning.
 - Review the [Instant Cluster configuration reference](/instant-clusters/configuration) for full details on environment variables and networking.