[FlashInfer v0.6.4] [RL] Integrate FlashInfer mxfp8 gemm, MoE, and routed MoE (#19537)

docs/advanced_features/expert_parallelism.md

@@ -32,6 +32,7 @@ Currently, DeepEP, Mooncake, `ascend_fuseep` and MORI only support cases where `
 | `deep_gemm`              | DeepGEMM backend optimized for MoE matrix multiplications, supporting contiguous layouts for prefill and masked layouts for decode; often JIT-compiled for performance. | Large-scale EP deployments with FP8 block-wise quantization. |
 | `cutlass`                | CUTLASS-based backend for efficient GEMMs. | NVIDIA architectures with CUTLASS support. |
 | `flashinfer_trtllm`      | FlashInfer integrated with TensorRT-LLM for accelerated MoE computations, supporting FP4 communication operators and high-performance GEMMs. | Blackwell with TRT-LLM. |
+| `flashinfer_trtllm_routed` | FlashInfer integrated with TensorRT-LLM for accelerated routed MoE computations, consuming SGLang-computed top-k expert assignments and weights. | Blackwell with TRT-LLM. |
 | `flashinfer_cutlass`     | FlashInfer combined with CUTLASS for high-performance grouped GEMMs in MoE layers, handling FP4/FP8 quantization efficiently. | Blackwell with FP4/FP8 models. |
 | `flashinfer_mxfp4`       | FlashInfer variant optimized for MXFP4 (mixed FP4) quantization in MoE runners, focusing on memory-efficient low-precision inference. | Low-precision models with MXFP4. |
 | `flashinfer_cutedsl`     | FlashInfer with a custom DSL for flexible and efficient MoE kernel generation, integrated with ModelOpt FP4 quantization. | Low-precision models with NVFP4. |