diff --git a/vllm/envs.py b/vllm/envs.py
index e4cf6a02..4f7a7ad7 100644
--- a/vllm/envs.py
+++ b/vllm/envs.py
@@ -137,7 +137,10 @@ environment_variables: Dict[str, Callable[[], Any]] = {
             os.path.join(get_default_cache_root(), "vllm"),
         )),
 
-    # used in distributed environment to determine the master address
+    # used in distributed environment to determine the ip address
+    # of the current node, when the node has multiple network interfaces.
+    # If you are using multi-node inference, you should set this differently
+    # on each node.
     'VLLM_HOST_IP':
     lambda: os.getenv('VLLM_HOST_IP', "") or os.getenv("HOST_IP", ""),
 
diff --git a/vllm/executor/ray_gpu_executor.py b/vllm/executor/ray_gpu_executor.py
index aec6998d..760c06cb 100644
--- a/vllm/executor/ray_gpu_executor.py
+++ b/vllm/executor/ray_gpu_executor.py
@@ -218,6 +218,19 @@ class RayGPUExecutor(DistributedGPUExecutor):
         for node_id, gpu_ids in node_gpus.items():
             node_gpus[node_id] = sorted(gpu_ids)
 
+        all_ips = set(worker_ips + [driver_ip])
+        n_ips = len(all_ips)
+        n_nodes = len(node_workers)
+
+        if n_nodes != n_ips:
+            raise RuntimeError(
+                f"Every node should have a unique IP address. Got {n_nodes}"
+                f" nodes with node ids {list(node_workers.keys())} and "
+                f"{n_ips} unique IP addresses {all_ips}. Please check your"
+                " network configuration. If you set `VLLM_HOST_IP` or "
+                "`HOST_IP` environment variable, make sure it is unique for"
+                " each node.")
+
         VLLM_INSTANCE_ID = get_vllm_instance_id()
 
         # Set environment variables for the driver and workers.