diff --git a/vllm/model_executor/models/qwen2_5_vl.py b/vllm/model_executor/models/qwen2_5_vl.py
index 4662176a1cc5..b0557d58d6dd 100644
--- a/vllm/model_executor/models/qwen2_5_vl.py
+++ b/vllm/model_executor/models/qwen2_5_vl.py
@@ -291,25 +291,6 @@ def forward(self, x: torch.Tensor):
         return x_down
 
 
-def all_gather_interleave(local_tensor, hidden_size: int, tp_size: int):
-    """All-gather the input tensor interleavely across model parallel group."""
-    import torch.distributed as dist
-
-    gathered_tensors = [torch.zeros_like(local_tensor) for _ in range(tp_size)]
-    dist.all_gather(
-        gathered_tensors, local_tensor, group=parallel_state.get_tp_group().device_group
-    )
-
-    gathered_tensors_split = [
-        torch.split(tensor, hidden_size // tp_size, -1) for tensor in gathered_tensors
-    ]
-    ordered_tensors = [
-        tensor for pair in zip(*gathered_tensors_split) for tensor in pair
-    ]
-    result_tensor = torch.cat(ordered_tensors, dim=-1)
-    return result_tensor
-
-
 class Qwen2_5_VisionAttention(nn.Module):
     def __init__(
         self,
@@ -383,21 +364,10 @@ def __init__(
     def split_qkv(self, qkv: torch.Tensor) -> tuple[torch.Tensor, ...]:
         # [s, b, 3 * head * head_dim]
         seq_len, bs, _ = qkv.shape
-        if self.tp_size > 1:
-            qkv = all_gather_interleave(qkv, self.qkv.hidden_size, self.tp_size)
 
         # [s, b, 3 * head * head_dim] -> 3 * [s, b, head * head_dim]
         q, k, v = qkv.chunk(3, dim=2)
 
-        # 3 * [s, b, head * head_dim]
-        if self.tp_size > 1:
-            splitter = partial(
-                dist_utils.split_tensor_along_last_dim, num_partitions=self.tp_size
-            )
-            q = splitter(q)[self.tp_rank]
-            k = splitter(k)[self.tp_rank]
-            v = splitter(v)[self.tp_rank]
-
         # 3 * [s, b, head * head_dim] -> 3 * [s, b, head, head_dim]
         new_shape = (
             seq_len,
diff --git a/vllm/model_executor/models/qwen2_vl.py b/vllm/model_executor/models/qwen2_vl.py
index bbebe7c0f928..ff04baee91d1 100644
--- a/vllm/model_executor/models/qwen2_vl.py
+++ b/vllm/model_executor/models/qwen2_vl.py
@@ -50,7 +50,7 @@
 )
 from vllm.config import VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
-from vllm.distributed import parallel_state, tensor_model_parallel_all_gather
+from vllm.distributed import parallel_state
 from vllm.distributed import utils as dist_utils
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import QuickGELU
@@ -396,21 +396,10 @@ def __init__(
     def split_qkv(self, qkv: torch.Tensor) -> tuple[torch.Tensor, ...]:
         # [s, b, 3 * head * head_dim]
         seq_len, bs, _ = qkv.shape
-        if self.tp_size > 1:
-            qkv = tensor_model_parallel_all_gather(qkv)
 
         # [s, b, 3 * head * head_dim] -> 3 * [s, b, head * head_dim]
         q, k, v = qkv.chunk(3, dim=2)
 
-        # 3 * [s, b, head * head_dim]
-        if self.tp_size > 1:
-            splitter = partial(
-                dist_utils.split_tensor_along_last_dim, num_partitions=self.tp_size
-            )
-            q = splitter(q)[self.tp_rank]
-            k = splitter(k)[self.tp_rank]
-            v = splitter(v)[self.tp_rank]
-
         # 3 * [s, b, head * head_dim] -> 3 * [s, b, head, head_dim]
         new_shape = (
             seq_len,