[Doc] Add Qwen3-235B tutorial

[JC-ut0]

What this PR does / why we need it?

Add Qwen3-235B tutorial including the following examples

• Single-node Online Deployment for 128k context inference
• Multi-node Deployment with MP

Does this PR introduce any user-facing change?

How was this patch tested?

• vLLM version: v0.12.0
• vLLM main: https://github.com/vllm-project/vllm/commit/v0.12.0

[github-actions]

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[gemini-code-assist]

Code Review

This pull request adds a new tutorial for running the Qwen3-235B model. The documentation is well-structured and provides good detail. I've found a couple of critical typos in model names within commands that would cause them to fail, and a potentially confusing or incorrect configuration for cudagraph_capture_sizes. I've left specific comments with suggestions to fix these issues.

[github-actions]

This pull request has conflicts, please resolve those before we can evaluate the pull request.

[leo-pony]

The example we provided represents the best practice under normal circumstances: optimal performance under stable operating conditions. Is this Capature size value a bit too small?

[JC-ut0]

the cudagraph_capture_sizes is set depending on the -max-num-seqs 4. This is an optimal example for 128k sequence inference.

[leo-pony]

The value of max-model-len should been 131072. I try to run this command but got following error:

(APIServer pid=598)   File "/vllm-workspace/vllm/vllm/engine/arg_utils.py", line 994, in create_model_config
(APIServer pid=598)     return ModelConfig(
(APIServer pid=598)            ^^^^^^^^^^^^
(APIServer pid=598)   File "/usr/local/python3.11.13/lib/python3.11/site-packages/pydantic/_internal/_dataclasses.py", line 121, in __init__
(APIServer pid=598)     s.__pydantic_validator__.validate_python(ArgsKwargs(args, kwargs), self_instance=s)
(APIServer pid=598) pydantic_core._pydantic_core.ValidationError: 1 validation error for ModelConfig
(APIServer pid=598)   Value error, User-specified max_model_len (133000) is greater than the derived max_model_len (max_position_embeddings=131072 or model_max_length=None in model's config.json). To allow overriding this maximum, set the env var VLLM_ALLOW_LONG_MAX_MODEL_LEN=1. VLLM_ALLOW_LONG_MAX_MODEL_LEN must be used with extreme caution. If the model uses relative position encoding (RoPE), positions exceeding derived_max_model_len lead to nan. If the model uses absolute position encoding, positions exceeding derived_max_model_len will cause a CUDA array out-of-bounds error. [type=value_error, input_value=ArgsKwargs((), {'model': ...rocessor_plugin': None}), input_type=ArgsKwargs]

[JC-ut0]

Did you add this parameter --rope-scaling '{"rope_type":"yarn","factor":4,"original_max_position_embeddings":32768}'