diff --git a/vllm/model_executor/layers/attention/mla_attention.py b/vllm/model_executor/layers/attention/mla_attention.py
index 239dbe4bef..2524296c6b 100644
--- a/vllm/model_executor/layers/attention/mla_attention.py
+++ b/vllm/model_executor/layers/attention/mla_attention.py
@@ -344,6 +344,20 @@ def _canonicalize_sparse_mla_kv_cache_dtype(
     return kv_cache_dtype
 
 
+def _get_kv_b_proj_input_dtype(
+    kv_b_proj: ColumnParallelLinear, use_fp8_prefill: bool
+) -> torch.dtype | None:
+    weight = getattr(kv_b_proj, "weight", None)
+    weight_dtype = weight.dtype if weight is not None else kv_b_proj.params_dtype
+    if weight_dtype == torch.int32:
+        return kv_b_proj.params_dtype
+    if weight_dtype == torch.uint8:
+        return None
+    if weight_dtype == current_platform.fp8_dtype() and not use_fp8_prefill:
+        return None
+    return weight_dtype
+
+
 class MLAAttention(nn.Module, AttentionLayerBase):
     """Multi-Head Latent Attention layer.
 
@@ -2271,6 +2285,9 @@ class MLACommonBaseImpl(MLAAttentionImpl[A], Generic[A]):
         assert prefill_metadata.chunked_context is not None
 
         use_fp8_prefill = prefill_metadata.q_data_type == current_platform.fp8_dtype()
+        kv_b_proj_input_dtype = _get_kv_b_proj_input_dtype(
+            self.kv_b_proj, use_fp8_prefill
+        )
 
         output = None
         merge_output = None
@@ -2316,21 +2333,8 @@ class MLACommonBaseImpl(MLAAttentionImpl[A], Generic[A]):
 
             # Extract kv_c_normed from workspace
             kv_c_normed = workspace[:toks][..., : self.kv_lora_rank]
-            # When FP8 weights are used without FP8 prefill, kv_b_proj expects
-            # model dtype input and will quantize internally.
-            # For quantized layers (AWQ/GPTQ) that lack a .weight attribute,
-            # use params_dtype which is the expected input dtype.
-            _kv_b_proj_w_dtype = (
-                self.kv_b_proj.weight.dtype
-                if hasattr(self.kv_b_proj, "weight")
-                else self.kv_b_proj.params_dtype
-            )
-            # For NVFP4, weights are packed uint8 — keep input in model dtype
-            # since the NVFP4 linear layer quantizes internally.
-            if (
-                use_fp8_prefill or _kv_b_proj_w_dtype != current_platform.fp8_dtype()
-            ) and _kv_b_proj_w_dtype != torch.uint8:
-                kv_c_normed = kv_c_normed.to(self.kv_b_proj.weight.dtype)
+            if kv_b_proj_input_dtype is not None:
+                kv_c_normed = kv_c_normed.to(kv_b_proj_input_dtype)
 
             k_pe = workspace[:toks][..., self.kv_lora_rank :].unsqueeze(1)
             kv_nope = self.kv_b_proj(kv_c_normed)[0].view(
@@ -2401,6 +2405,9 @@ class MLACommonBaseImpl(MLAAttentionImpl[A], Generic[A]):
         assert prefill_metadata.chunked_context.chunk_size is not None
 
         use_fp8_prefill = prefill_metadata.q_data_type == current_platform.fp8_dtype()
+        kv_b_proj_input_dtype = _get_kv_b_proj_input_dtype(
+            self.kv_b_proj, use_fp8_prefill
+        )
         output = None
         merge_output = None
         iters = len(prefill_metadata.chunked_context.seq_tot)
@@ -2482,16 +2489,8 @@ class MLACommonBaseImpl(MLAAttentionImpl[A], Generic[A]):
                 chunk_idx=i,
                 toks=toks,
             )
-
-            kv_b_proj_w_dtype = (
-                self.kv_b_proj.weight.dtype
-                if hasattr(self.kv_b_proj, "weight")
-                else self.kv_b_proj.params_dtype
-            )
-            if (
-                use_fp8_prefill or kv_b_proj_w_dtype != current_platform.fp8_dtype()
-            ) and kv_b_proj_w_dtype != torch.uint8:
-                kv_c_normed = kv_c_normed.to(kv_b_proj_w_dtype)
+            if kv_b_proj_input_dtype is not None:
+                kv_c_normed = kv_c_normed.to(kv_b_proj_input_dtype)
 
             kv_nope = self.kv_b_proj(kv_c_normed)[0].view(
                 -1, self.num_heads, self.qk_nope_head_dim + self.v_head_dim