diff --git a/vllm/model_executor/layers/mamba/mamba_mixer.py b/vllm/model_executor/layers/mamba/mamba_mixer.py
index 596c74c73..c22a309ce 100644
--- a/vllm/model_executor/layers/mamba/mamba_mixer.py
+++ b/vllm/model_executor/layers/mamba/mamba_mixer.py
@@ -34,6 +34,7 @@ from vllm.model_executor.layers.mamba.ops.mamba_ssm import (
     selective_state_update,
 )
 from vllm.model_executor.utils import set_weight_attrs
+from vllm.platforms import current_platform
 from vllm.utils.torch_utils import direct_register_custom_op
 from vllm.v1.attention.backends.mamba1_attn import Mamba1AttentionMetadata
 
@@ -195,11 +196,12 @@ class MambaMixer(MambaBase, CustomOp):
     def _ssm_transform(
         self, x: torch.Tensor
     ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        if self.is_lora_enabled:
-            #  Lora kernel requires contiguous tensor.
-            ssm_params = self.x_proj(x.contiguous())[0]
-        else:
-            ssm_params = self.x_proj(x)[0]
+        # LoRA kernel requires contiguous tensor.
+        # ROCm: Non-contiguous tensors cause incorrect GEMM
+        # results when batch > 1.
+        if self.is_lora_enabled or current_platform.is_rocm():
+            x = x.contiguous()
+        ssm_params = self.x_proj(x)[0]
         time_step, B, C = torch.split(
             ssm_params,
             [self.time_step_rank, self.ssm_state_size, self.ssm_state_size],
diff --git a/vllm/model_executor/models/plamo2.py b/vllm/model_executor/models/plamo2.py
index 225e131ec..45512d23d 100644
--- a/vllm/model_executor/models/plamo2.py
+++ b/vllm/model_executor/models/plamo2.py
@@ -63,6 +63,7 @@ from vllm.model_executor.models.utils import (
     maybe_prefix,
 )
 from vllm.model_executor.utils import set_weight_attrs
+from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors
 from vllm.utils.torch_utils import direct_register_custom_op
 from vllm.v1.attention.backend import AttentionMetadata
@@ -414,6 +415,13 @@ class Plamo2MambaMixer(MambaBase, CustomOp):
                 conv_state_indices=state_indices_tensor_d,
             )
 
+            # ROCm: Ensure contiguous tensor for bcdt_proj linear layer.
+            # causal_conv1d_update returns a non-contiguous view (stride 8192
+            # instead of 4096 for shape [batch, 4096]), causing incorrect GEMM
+            # results when batch > 1 on ROCm.
+            if current_platform.is_rocm():
+                hidden_states_d = hidden_states_d.contiguous()
+
             B, C, dt = self._project_ssm_parameters(hidden_states_d)
 
             # 3. State Space Model sequence transformation