diff --git a/tests/models/language/generation/test_hybrid.py b/tests/models/language/generation/test_hybrid.py
index d0e42062099e..206ad1352e06 100644
--- a/tests/models/language/generation/test_hybrid.py
+++ b/tests/models/language/generation/test_hybrid.py
@@ -418,7 +418,9 @@ def test_full_cuda_graph(
 @pytest.mark.parametrize("model", FP32_STATE_MODELS)
 @pytest.mark.parametrize("max_tokens", [64])
 @pytest.mark.parametrize("num_logprobs", [5])
-def test_fp32_state(
+@pytest.mark.parametrize("cache_dtype_param",
+                         ["mamba_ssm_cache_dtype", "mamba_cache_dtype"])
+def test_fp32_cache_state(
     hf_runner,
     vllm_runner,
     example_prompts,
@@ -426,6 +428,7 @@ def test_fp32_state(
     model: str,
     max_tokens: int,
     num_logprobs: int,
+    cache_dtype_param: str,
 ) -> None:
 
     try:
@@ -443,13 +446,13 @@ def test_fp32_state(
         m.setenv("VLLM_USE_V1", "0")
         with vllm_runner(model,
                          max_num_seqs=MAX_NUM_SEQS,
-                         mamba_ssm_cache_dtype="float32") as vllm_model:
+                         **{cache_dtype_param: "float32"}) as vllm_model:
             vllm_v0_outputs = vllm_model.generate_greedy_logprobs(
                 example_prompts, max_tokens, num_logprobs)
 
     with vllm_runner(model,
                      max_num_seqs=MAX_NUM_SEQS,
-                     mamba_ssm_cache_dtype="float32") as vllm_model:
+                     **{cache_dtype_param: "float32"}) as vllm_model:
         vllm_v1_outputs = vllm_model.generate_greedy_logprobs(
             example_prompts, max_tokens, num_logprobs)
 
diff --git a/vllm/model_executor/layers/mamba/ops/causal_conv1d.py b/vllm/model_executor/layers/mamba/ops/causal_conv1d.py
index 7e3ea561fd29..cb0c52d39d00 100644
--- a/vllm/model_executor/layers/mamba/ops/causal_conv1d.py
+++ b/vllm/model_executor/layers/mamba/ops/causal_conv1d.py
@@ -415,6 +415,9 @@ def causal_conv1d_fn(
         activation = "silu"
 
     args = None
+    # Store original dtype to cast back at the end
+    original_x_dtype = x.dtype
+    x = x.to(conv_states.dtype)
     out = torch.empty_like(x)
     if metadata is not None:
         cu_seqlen = metadata.cu_seqlen
@@ -613,7 +616,7 @@ def grid(META):
         BLOCK_N=256,
         num_stages=2,
     )
-    return out
+    return out.to(original_x_dtype)
 
 
 @triton.jit()
@@ -971,6 +974,9 @@ def causal_conv1d_update(
         activation = "silu" if activation is True else None
     elif activation is not None:
         assert activation in ["silu", "swish"]
+
+    original_x_dtype = x.dtype
+    x = x.to(conv_state.dtype)
     unsqueeze = query_start_loc is None and x.dim() == 2
     if unsqueeze:
         # make it (batch, dim, seqlen) with seqlen == 1
@@ -1079,4 +1085,4 @@ def grid(META):
     )
     if unsqueeze:
         out = out.squeeze(-1)
-    return out
+    return out.to(original_x_dtype)