Add MoE config for Super B200 TP2

[shaharmor98]

When locally running Nemotron Super on B200 the following warning appears:

Using default MoE config. Performance might be sub-optimal!

I used the benchmark_moe.py to create a JSON file for this use-case:

python benchmarks/kernels/benchmark_moe.py \
  --model $MODEL_PATH \
  --trust-remote-code \
  --tp-size 2 \
  --tune \
  --batch-size 1 2 4 8 16 24 32 48 64 96 128 256 512 768 1024 1536 \
  --save-dir /.../vllm/model_executor/layers/fused_moe/configs/

Related PRs:
#27967

Test Plan

Compare performance (vllm bench serve) with various batch sizes, with and without the JSON file.

Performance should be equal or better when the JSON is available.

Test Result

Absolute output tokens per second cannot be disclosed at this stage.
Instead, we'd report the gained diff.

Setup for all benchmarks: B200, TP2

Concurrency (Batch Size)	Difference
16	+1.6%
64	+4.1%
128	+9.3%
512	+24.9%

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[dosubot]

Related Documentation

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[gemini-code-assist]

Code Review

This pull request introduces a new Mixture of Experts (MoE) configuration file for the NVIDIA B200 GPU with a tensor parallelism size of 2. The configuration is generated by the project's benchmarking script and aims to optimize performance for MoE models on this specific hardware. The provided performance metrics show a significant improvement with the new configuration. The change is straightforward and appears to be a valuable performance enhancement.

[mgoin]

LGTM, thanks for including benchmarks!