diff --git a/vllm/distributed/kv_transfer/kv_connector/utils.py b/vllm/distributed/kv_transfer/kv_connector/utils.py
index b8eb5ea3b493..b2c2c0e6b596 100644
--- a/vllm/distributed/kv_transfer/kv_connector/utils.py
+++ b/vllm/distributed/kv_transfer/kv_connector/utils.py
@@ -8,9 +8,7 @@
 
 import torch
 
-import vllm.envs as envs
-from vllm import _custom_ops as ops
-from vllm.config import VllmConfig, get_current_vllm_config
+from vllm.config import get_current_vllm_config
 from vllm.distributed.kv_transfer.kv_connector.factory import KVConnectorFactory
 from vllm.logger import init_logger
 from vllm.v1.outputs import KVConnectorOutput, ModelRunnerOutput
@@ -21,89 +19,6 @@
 logger = init_logger(__name__)
 
 
-class model_aware_kv_ops_helper:
-    def __init__(self, config: VllmConfig):
-        self.is_deepseek_mla = config.model_config.is_deepseek_mla
-        self.use_mla_opt = not envs.VLLM_MLA_DISABLE
-        self.tp_size = config.parallel_config.tensor_parallel_size
-
-    def get_model_args(self, model_executable: torch.nn.Module):
-        model_config = model_executable.model.config
-        self.model_executable = model_executable
-        num_heads = int(model_config.num_key_value_heads / self.tp_size)
-        hidden_size = model_config.hidden_size
-        num_attention_heads = model_config.num_attention_heads
-
-        # Deepseek's MLA (Multi-head Latent Attention) uses two different
-        # kv_cache shapes based on whether VLLM_MLA_DISABLE is set to 0.
-        # When VLLM_MLA_DISABLE=0 (default), forward absorb is applied,
-        # resulting in a kv_cache shape of [num_blks, blk_size, 1,
-        # kv_lora_rank + qk_rope_head_dim].
-        # When VLLM_MLA_DISABLE=1, standard FA is used instead, leading
-        # to a kv_cache shape of [2, num_blks, blk_size,
-        # num_key_value_heads / tp, qk_nope_head_dim + qk_rope_head_dim].
-        # For more details, see vllm/v1/attention/backends/mla/common.py.
-        if self.is_deepseek_mla and self.use_mla_opt:
-            head_size = model_config.kv_lora_rank + model_config.qk_rope_head_dim
-            num_heads = 1
-        elif self.is_deepseek_mla and not self.use_mla_opt:
-            head_size = model_config.qk_nope_head_dim + model_config.qk_rope_head_dim
-        else:
-            head_size = getattr(model_config, "head_dim", None)
-            if head_size is None:
-                head_size = int(hidden_size // num_attention_heads)
-
-        return num_heads, head_size
-
-    def get_kv_from_cache(self, kv_cache, num_heads, head_size):
-        if self.is_deepseek_mla and self.use_mla_opt:
-            key_cache = kv_cache.reshape(-1, num_heads, head_size)
-            value_cache = kv_cache.reshape(-1, num_heads, head_size)
-        else:
-            key_cache = kv_cache[0].reshape(-1, num_heads, head_size)
-            value_cache = kv_cache[1].reshape(-1, num_heads, head_size)
-        return key_cache, value_cache
-
-    def put_kv_to_cache(
-        self,
-        model_executable: torch.nn.Module,
-        keys,
-        values,
-        layer,
-        kv_cache,
-        slot_mapping,
-        start_pos,
-        end_pos,
-    ):
-        model_config = model_executable.model.config
-
-        if self.is_deepseek_mla and self.use_mla_opt:
-            layer.self_attn.attn = layer.self_attn.mla_attn
-            k_c_normed_k_pe = keys.squeeze(1)
-            k_c_normed = k_c_normed_k_pe[:, : model_config.kv_lora_rank]
-            k_pe = k_c_normed_k_pe[:, model_config.kv_lora_rank :]
-            ops.concat_and_cache_mla(
-                k_c_normed.to(kv_cache.device),
-                k_pe.to(kv_cache.device),
-                kv_cache,
-                slot_mapping[start_pos:end_pos],
-                layer.self_attn.attn.kv_cache_dtype,
-                layer.self_attn.attn._k_scale,
-            )
-        else:
-            key_cache, value_cache = kv_cache[0], kv_cache[1]
-            ops.reshape_and_cache_flash(
-                keys.to(key_cache.device),
-                values.to(value_cache.device),
-                key_cache,
-                value_cache,
-                slot_mapping[start_pos:end_pos],
-                layer.self_attn.attn.kv_cache_dtype,
-                layer.self_attn.attn._k_scale,
-                layer.self_attn.attn._v_scale,
-            )
-
-
 def get_kv_connector_cache_layout():
     # NOTE (NickLucche) When running disaggregated PD with NIXL, HND layout is
     # used for faster transfer.