diff --git a/vllm/model_executor/layers/fused_moe/gpt_oss_triton_kernels_moe.py b/vllm/model_executor/layers/fused_moe/gpt_oss_triton_kernels_moe.py
index badedfc54..e05c0eea4 100644
--- a/vllm/model_executor/layers/fused_moe/gpt_oss_triton_kernels_moe.py
+++ b/vllm/model_executor/layers/fused_moe/gpt_oss_triton_kernels_moe.py
@@ -20,9 +20,16 @@ logger = init_logger(__name__)
 if has_triton_kernels():
     try:
         import triton_kernels.swiglu
-        from triton_kernels.matmul_ogs import FnSpecs, FusedActivation, matmul_ogs
-        from triton_kernels.routing import RoutingData, routing, routing_from_bitmatrix
-        from triton_kernels.tensor import Bitmatrix
+        from triton_kernels.matmul_ogs import (
+            FnSpecs,
+            FusedActivation,
+            GatherIndx,
+            RoutingData,
+            ScatterIndx,
+            matmul_ogs,
+        )
+        from triton_kernels.tensor import BIT, Bitmatrix, SparseMatrix, make_ragged_tensor_metadata
+        from triton_kernels.topk import topk as triton_topk
     except (AttributeError, ImportError) as e:
         logger.error(
             "Failed to import Triton kernels. Please make sure your triton "
@@ -84,8 +91,17 @@ def triton_kernel_moe_forward(
     global_num_experts: int = -1,
     expert_map: torch.Tensor | None = None,
 ) -> torch.Tensor:
-    routing_data, gather_idx, scatter_idx = routing(
-        gating_output, topk, sm_first=not renormalize
+    # Use new topk API instead of deprecated routing
+    sm_first = not renormalize
+    if sm_first:
+        gating_output = torch.softmax(gating_output, dim=-1)
+    sparse_logits = triton_topk(
+        gating_output, topk, apply_softmax=not sm_first, y_indx=None, n_rows=None
+    )
+
+    # Convert to routing data using the existing make_routing_data function
+    routing_data, gather_idx, scatter_idx = make_routing_data(
+        sparse_logits.indx, sparse_logits.vals, gating_output.shape[-1]
     )
 
     return triton_kernel_fused_experts(
@@ -202,14 +218,29 @@ def make_routing_data(
     bitmatrix_shape = [n_rows, bm_cols * 32]
     bitmatrix_shape_max = [n_rows, None]
     bitmatrix = Bitmatrix(
-        bitmatrix, shape=bitmatrix_shape, shape_max=bitmatrix_shape_max, scratchpad=None
+        bitmatrix, dtype=BIT, shape=bitmatrix_shape, shape_max=bitmatrix_shape_max
     )
 
     # matmul_ogs expects invalid topk_weights to be -1s
     topk_weights = torch.where(topk_ids == -1, -1.0, topk_weights)
-    routing_data, gather_indx, scatter_indx = routing_from_bitmatrix(
-        bitmatrix, topk_weights, topk_ids, num_local_experts, num_topk
+
+    # Use new SparseMatrix API instead of deprecated routing_from_bitmatrix
+    sparse_logits = SparseMatrix(indx=topk_ids, vals=topk_weights, mask=bitmatrix)
+    dispatch_indx = sparse_logits.mask_metadata.col_sorted_indx
+    combine_indx = sparse_logits.mask_metadata.row_sorted_indx
+    ragged_batch_metadata = make_ragged_tensor_metadata(
+        sparse_logits.mask_metadata.col_sum, dispatch_indx.shape[0]
+    )
+    gate_scal = sparse_logits.vals.flatten()[combine_indx]
+    routing_data = RoutingData(
+        gate_scal,
+        ragged_batch_metadata.block_sizes,
+        num_local_experts,
+        num_topk,
+        ragged_batch_metadata,
     )
+    gather_indx = GatherIndx(combine_indx, dispatch_indx)
+    scatter_indx = ScatterIndx(dispatch_indx, combine_indx)
 
     return routing_data, gather_indx, scatter_indx