diff --git a/vllm/model_executor/layers/attention/mla_attention.py b/vllm/model_executor/layers/attention/mla_attention.py
index 3794bde41..36ee728dc 100644
--- a/vllm/model_executor/layers/attention/mla_attention.py
+++ b/vllm/model_executor/layers/attention/mla_attention.py
@@ -442,6 +442,7 @@ class MLAAttention(nn.Module, AttentionLayerBase):
         # If kv_b_proj_weight is unquantized, quantize it to mxfp4 if supported
         self.is_aiter_triton_fp4_bmm_enabled = (
             rocm_aiter_ops.is_fp4bmm_enabled()
+            and hasattr(self.kv_b_proj, "weight")
             and self.kv_b_proj.weight.dtype == torch.bfloat16
         )
 
@@ -2492,11 +2493,15 @@ class MLACommonImpl(MLAAttentionImpl[M], Generic[M]):
             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.
-            if (
-                use_fp8_prefill
-                or self.kv_b_proj.weight.dtype != current_platform.fp8_dtype()
-            ):
-                kv_c_normed = kv_c_normed.to(self.kv_b_proj.weight.dtype)
+            # 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
+            )
+            if use_fp8_prefill or _kv_b_proj_w_dtype != current_platform.fp8_dtype():
+                kv_c_normed = kv_c_normed.to(_kv_b_proj_w_dtype)
 
             k_pe = workspace[:toks][..., self.kv_lora_rank :].unsqueeze(1)
             kv_nope = self.kv_b_proj(kv_c_normed)[0].view(