diff --git a/docs/source/getting_started/debugging.rst b/docs/source/getting_started/debugging.rst
new file mode 100644
index 00000000..6199b726
--- /dev/null
+++ b/docs/source/getting_started/debugging.rst
@@ -0,0 +1,36 @@
+.. _debugging:
+
+Debugging Tips
+===============
+
+Debugging hang/crash issues
+---------------------------
+
+When an vLLM instance hangs or crashes, it is very difficult to debug the issue. Here are some tips to help debug the issue:
+
+- Set the environment variable ``export VLLM_LOGGING_LEVEL=DEBUG`` to turn on more logging.
+- Set the environment variable ``export CUDA_LAUNCH_BLOCKING=1`` to know exactly which CUDA kernel is causing the trouble.
+- Set the environment variable ``export NCCL_DEBUG=TRACE`` to turn on more logging for NCCL.
+- Set the environment variable ``export VLLM_TRACE_FUNCTION=1`` . All the function calls in vLLM will be recorded. Inspect these log files, and tell which function crashes or hangs. **Note: it will generate a lot of logs and slow down the system. Only use it for debugging purposes.**
+
+With more logging, hopefully you can find the root cause of the issue.
+
+Here are some common issues that can cause hangs:
+
+- The network setup is incorrect. The vLLM instance cannot get the correct IP address. You can find the log such as ``DEBUG 06-10 21:32:17 parallel_state.py:88] world_size=8 rank=0 local_rank=0 distributed_init_method=tcp://xxx.xxx.xxx.xxx:54641 backend=nccl``. The IP address should be the correct one. If not, override the IP address by setting the environment variable ``export VLLM_HOST_IP=your_ip_address``.
+- Hardware/driver setup is incorrect. GPU communication cannot be established. You can run a sanity check script below to see if the GPU communication is working correctly.
+
+.. code-block:: python
+
+    # save it as `test.py`` , and run it with `NCCL_DEBUG=TRACE torchrun --nproc-per-node=8 test.py`
+    # adjust `--nproc-per-node` to the number of GPUs you want to use.
+    import torch
+    import torch.distributed as dist
+    dist.init_process_group(backend="nccl")
+    data = torch.FloatTensor([1,] * 128).to(f"cuda:{dist.get_rank()}")
+    dist.all_reduce(data, op=dist.ReduceOp.SUM)
+    torch.cuda.synchronize()
+    value = data.mean().item()
+    assert value == dist.get_world_size()
+
+If the problem persists, feel free to open an `issue <https://github.com/vllm-project/vllm/issues/new/choose>`_ on GitHub, with a detailed description of the issue, your environment, and the logs.
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 0ebd1fb7..0ff0ea1d 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -66,6 +66,7 @@ Documentation
    getting_started/neuron-installation
    getting_started/cpu-installation
    getting_started/quickstart
+   getting_started/debugging
    getting_started/examples/examples_index
 
 .. toctree::