[doc] improve debugging doc (#10270)

Signed-off-by: youkaichao <youkaichao@gmail.com>

docs/source/getting_started/debugging.rst

@@ -20,6 +20,10 @@ Hangs loading a model from disk
 If the model is large, it can take a long time to load it from disk. Pay attention to where you store the model. Some clusters have shared filesystems across nodes, e.g. a distributed filesystem or a network filesystem, which can be slow. 
 It'd be better to store the model in a local disk. Additionally, have a look at the CPU memory usage, when the model is too large it might take a lot of CPU memory, slowing down the operating system because it needs to frequently swap between disk and memory.
 
+.. note::
+
+    To isolate the model downloading and loading issue, you can use the ``--load-format dummy`` argument to skip loading the model weights. This way, you can check if the model downloading and loading is the bottleneck.
+
 Model is too large
 ----------------------------------------
 If the model is too large to fit in a single GPU, you might want to `consider tensor parallelism <https://docs.vllm.ai/en/latest/serving/distributed_serving.html#distributed-inference-and-serving>`_ to split the model across multiple GPUs. In that case, every process will read the whole model and split it into chunks, which makes the disk reading time even longer (proportional to the size of tensor parallelism). You can convert the model checkpoint to a sharded checkpoint using `this example <https://docs.vllm.ai/en/latest/getting_started/examples/save_sharded_state.html>`_ . The conversion process might take some time, but later you can load the sharded checkpoint much faster. The model loading time should remain constant regardless of the size of tensor parallelism.