diff --git a/vllm/model_executor/models/deepseek_mtp.py b/vllm/model_executor/models/deepseek_mtp.py
index 87364b1f85c3..c75ee1a1bbfe 100644
--- a/vllm/model_executor/models/deepseek_mtp.py
+++ b/vllm/model_executor/models/deepseek_mtp.py
@@ -241,9 +241,6 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
             ("gate_up_proj", "up_proj", 1),
             ("fused_qkv_a_proj", "q_a_proj", 0),
             ("fused_qkv_a_proj", "kv_a_proj_with_mqa", 1),
-            # Fused indexer wk + weights_proj
-            ("wk_weights_proj", "wk", 0),
-            ("wk_weights_proj", "weights_proj", 1),
         ]
 
         expert_params_mapping = SharedFusedMoE.make_expert_params_mapping(
diff --git a/vllm/model_executor/models/deepseek_v2.py b/vllm/model_executor/models/deepseek_v2.py
index f50e38b60f8e..f1c4a7b21993 100644
--- a/vllm/model_executor/models/deepseek_v2.py
+++ b/vllm/model_executor/models/deepseek_v2.py
@@ -639,19 +639,21 @@ def __init__(
             quant_config=quant_config,
             prefix=f"{prefix}.wq_b",
         )
-        # Fused wk + weights_proj: single GEMM producing [head_dim + n_head].
-        # weights_proj does not get quantized, so we run both with quant_config=None
-        # wk may be upcasted from the default quant; experiments show fusion is always
-        # faster unless WK proj is in FP4, which is not the case for all known quants.
-        self.wk_weights_proj = MergedColumnParallelLinear(
+        self.wk = ReplicatedLinear(
             hidden_size,
-            [self.head_dim, self.n_head],
+            self.head_dim,
             bias=False,
-            quant_config=None,
-            disable_tp=True,
-            prefix=f"{prefix}.wk_weights_proj",
+            quant_config=quant_config,
+            prefix=f"{prefix}.wk",
         )
         self.k_norm = LayerNorm(self.head_dim, eps=1e-6)
+        self.weights_proj = ReplicatedLinear(
+            hidden_size,
+            self.n_head,
+            bias=False,
+            quant_config=None,
+            prefix=f"{prefix}.weights_proj",
+        )
         self.softmax_scale = self.head_dim**-0.5
 
         self.scale_fmt = "ue8m0"
@@ -692,11 +694,7 @@ def forward(
             q, [self.rope_dim, self.head_dim - self.rope_dim], dim=-1
         )
 
-        # Fused wk + weights_proj: one GEMM, then split
-        kw, _ = self.wk_weights_proj(hidden_states)
-        k = kw[:, : self.head_dim]
-        weights_raw = kw[:, self.head_dim :]
-
+        k, _ = self.wk(hidden_states)
         k = self.k_norm(k)
         k_pe, k_nope = torch.split(
             k, [self.rope_dim, self.head_dim - self.rope_dim], dim=-1
@@ -725,8 +723,9 @@ def forward(
         q_fp8 = q_fp8.view(-1, self.n_head, self.head_dim)
         q_scale = q_scale.view(-1, self.n_head, 1)
 
+        weights, _ = self.weights_proj(hidden_states)
         weights = (
-            weights_raw.unsqueeze(-1) * q_scale * self.softmax_scale * self.n_head**-0.5
+            weights.unsqueeze(-1) * q_scale * self.softmax_scale * self.n_head**-0.5
         )
         weights = weights.squeeze(-1)
 
@@ -1439,13 +1438,6 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
             ("qkv_proj", "k_proj", "k"),
             ("qkv_proj", "v_proj", "v"),
         ]
-        # Fused indexer wk + weights_proj (shard 0 = wk, shard 1 = weights_proj)
-        indexer_fused_mapping = [
-            ("wk_weights_proj", "wk", 0),
-            ("wk_weights_proj", "weights_proj", 1),
-        ]
-        stacked_params_mapping.extend(indexer_fused_mapping)
-
         if self.use_mha:
             stacked_params_mapping.extend(mha_params_mapping)
         else: