diff --git a/tests/models/language/generation/test_hybrid.py b/tests/models/language/generation/test_hybrid.py
index 7e7cc893ec8a..31ca3a6f0f98 100644
--- a/tests/models/language/generation/test_hybrid.py
+++ b/tests/models/language/generation/test_hybrid.py
@@ -100,21 +100,19 @@ def test_models(
         else:
             hf_outputs = None
 
-    if model not in V0_UNSUPPORTED_MODELS:
-        with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
-            vllm_v0_outputs = vllm_model.generate_greedy_logprobs(
-                example_prompts, max_tokens, num_logprobs)
-    else:
-        vllm_v0_outputs = None
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_USE_V1", "0")
+        if model not in V0_UNSUPPORTED_MODELS:
+            with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
+                vllm_v0_outputs = vllm_model.generate_greedy_logprobs(
+                    example_prompts, max_tokens, num_logprobs)
+        else:
+            vllm_v0_outputs = None
 
     if model in V1_SUPPORTED_MODELS:
-        with monkeypatch.context() as m:
-            m.setenv("VLLM_USE_V1", "1")
-            with vllm_runner(model,
-                             max_num_seqs=MAX_NUM_SEQS,
-                             enable_prefix_caching=False) as vllm_model:
-                vllm_v1_outputs = vllm_model.generate_greedy_logprobs(
-                    example_prompts, max_tokens, num_logprobs)
+        with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
+            vllm_v1_outputs = vllm_model.generate_greedy_logprobs(
+                example_prompts, max_tokens, num_logprobs)
     else:
         vllm_v1_outputs = None
 
@@ -137,7 +135,7 @@ def test_models(
         )
 
 
-@pytest.mark.parametrize("model", SSM_MODELS + HYBRID_MODELS)
+@pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
 @pytest.mark.parametrize("max_tokens", [64])
 @pytest.mark.parametrize("num_logprobs", [5])
 def test_batching(
@@ -147,10 +145,6 @@ def test_batching(
     max_tokens: int,
     num_logprobs: int,
 ) -> None:
-    if model in V0_UNSUPPORTED_MODELS:
-        pytest.skip(
-            f"Unsupported V0 Engine. Skipping `test_batching` on {model}.")
-
     try:
         model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
         model_info.check_available_online(on_fail="skip")
@@ -188,29 +182,32 @@ def test_chunked_prefill(
     max_tokens: int,
     num_logprobs: int,
     chunked_prefill_token_size: int,
+    monkeypatch,
 ) -> None:
     max_num_seqs = chunked_prefill_token_size
     max_num_batched_tokens = chunked_prefill_token_size
 
-    with vllm_runner(model,
-                     enable_chunked_prefill=True,
-                     max_num_batched_tokens=max_num_batched_tokens,
-                     max_num_seqs=max_num_seqs) as vllm_model:
-        chunked = vllm_model.generate_greedy_logprobs(example_prompts,
-                                                      max_tokens, num_logprobs)
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_USE_V1", "0")
+        with vllm_runner(model,
+                         enable_chunked_prefill=True,
+                         max_num_batched_tokens=max_num_batched_tokens,
+                         max_num_seqs=max_num_seqs) as vllm_model:
+            chunked = vllm_model.generate_greedy_logprobs(
+                example_prompts, max_tokens, num_logprobs)
 
-    with vllm_runner(model,
-                     enable_chunked_prefill=False,
-                     max_num_seqs=max_num_seqs) as vllm_model:
-        non_chunked = vllm_model.generate_greedy_logprobs(
-            example_prompts, max_tokens, num_logprobs)
+        with vllm_runner(model,
+                         enable_chunked_prefill=False,
+                         max_num_seqs=max_num_seqs) as vllm_model:
+            non_chunked = vllm_model.generate_greedy_logprobs(
+                example_prompts, max_tokens, num_logprobs)
 
-    check_logprobs_close(
-        outputs_0_lst=chunked,
-        outputs_1_lst=non_chunked,
-        name_0="chunked",
-        name_1="non_chunked",
-    )
+        check_logprobs_close(
+            outputs_0_lst=chunked,
+            outputs_1_lst=non_chunked,
+            name_0="chunked",
+            name_1="non_chunked",
+        )
 
 
 @pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
@@ -281,25 +278,29 @@ def test_models_preemption_recompute(
     example_prompts,
     model: str,
     max_tokens: int,
+    monkeypatch,
 ) -> None:
     """
     Tests that outputs are identical with and w/o preemptions (recompute).
     """
-    with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
-        scheduler = vllm_model.llm.llm_engine.scheduler[0]
-        scheduler.ENABLE_ARTIFICIAL_PREEMPT = True
-        preempt_vllm_outputs = vllm_model.generate_greedy(
-            example_prompts, max_tokens)
-
-        scheduler.ENABLE_ARTIFICIAL_PREEMPT = False
-        vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
-
-    check_outputs_equal(
-        outputs_0_lst=preempt_vllm_outputs,
-        outputs_1_lst=vllm_outputs,
-        name_0="vllm_preepmtions",
-        name_1="vllm",
-    )
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_USE_V1", "0")
+        with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
+            scheduler = vllm_model.llm.llm_engine.scheduler[0]
+            scheduler.ENABLE_ARTIFICIAL_PREEMPT = True
+            preempt_vllm_outputs = vllm_model.generate_greedy(
+                example_prompts, max_tokens)
+
+            scheduler.ENABLE_ARTIFICIAL_PREEMPT = False
+            vllm_outputs = vllm_model.generate_greedy(example_prompts,
+                                                      max_tokens)
+
+        check_outputs_equal(
+            outputs_0_lst=preempt_vllm_outputs,
+            outputs_1_lst=vllm_outputs,
+            name_0="vllm_preepmtions",
+            name_1="vllm",
+        )
 
 
 @pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
@@ -402,24 +403,18 @@ def test_full_cuda_graph(
         else:
             hf_outputs = None
 
-    if model not in V0_UNSUPPORTED_MODELS:
-        with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
-            vllm_v0_outputs = vllm_model.generate_greedy_logprobs(
-                example_prompts, max_tokens, num_logprobs)
-    else:
-        vllm_v0_outputs = None
-
     with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
-        if model in HYBRID_MODELS:
-            # required due to reorder_batch behaviour
-            m.setenv("VLLM_ATTENTION_BACKEND", "FLASHINFER")
-        with vllm_runner(model,
-                         max_num_seqs=MAX_NUM_SEQS,
-                         compilation_config={'full_cuda_graph': True},
-                         enable_prefix_caching=False) as vllm_model:
-            vllm_v1_outputs = vllm_model.generate_greedy_logprobs(
-                example_prompts, max_tokens, num_logprobs)
+        m.setenv("VLLM_USE_V1", "0")
+        if model not in V0_UNSUPPORTED_MODELS:
+            with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
+                vllm_v0_outputs = vllm_model.generate_greedy_logprobs(
+                    example_prompts, max_tokens, num_logprobs)
+        else:
+            vllm_v0_outputs = None
+
+    with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
+        vllm_v1_outputs = vllm_model.generate_greedy_logprobs(
+            example_prompts, max_tokens, num_logprobs)
 
     if hf_outputs is not None and vllm_v0_outputs is not None:
         check_logprobs_close(
@@ -466,24 +461,20 @@ def test_fp32_state(
         else:
             hf_outputs = None
 
-    with vllm_runner(model,
-                     max_num_seqs=MAX_NUM_SEQS,
-                     mamba_ssm_cache_dtype="float32") as vllm_model:
-        vllm_v0_outputs = vllm_model.generate_greedy_logprobs(
-            example_prompts, max_tokens, num_logprobs)
-
     with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
-        if model in HYBRID_MODELS:
-            # required due to reorder_batch behaviour
-            m.setenv("VLLM_ATTENTION_BACKEND", "FLASHINFER")
+        m.setenv("VLLM_USE_V1", "0")
         with vllm_runner(model,
                          max_num_seqs=MAX_NUM_SEQS,
-                         mamba_ssm_cache_dtype="float32",
-                         enable_prefix_caching=False) as vllm_model:
-            vllm_v1_outputs = vllm_model.generate_greedy_logprobs(
+                         mamba_ssm_cache_dtype="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:
+        vllm_v1_outputs = vllm_model.generate_greedy_logprobs(
+            example_prompts, max_tokens, num_logprobs)
+
     if hf_outputs is not None:
         check_logprobs_close(
             outputs_0_lst=hf_outputs,
diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py
index 9e7c95ea5205..5549b9a13754 100644
--- a/vllm/engine/arg_utils.py
+++ b/vllm/engine/arg_utils.py
@@ -1452,11 +1452,6 @@ def _is_v1_supported_oracle(self, model_config: ModelConfig) -> bool:
                                recommend_to_remove=False)
             return False
 
-        # V1 mamba models are unoptimized.
-        if model_config.has_inner_state and _warn_or_fallback(
-                feature_name="Mamba"):
-            return False
-
         # No Concurrent Partial Prefills so far.
         if (self.max_num_partial_prefills
                 != SchedulerConfig.max_num_partial_prefills
diff --git a/vllm/model_executor/models/config.py b/vllm/model_executor/models/config.py
index 88b3154de2cb..b0dbfacece3a 100644
--- a/vllm/model_executor/models/config.py
+++ b/vllm/model_executor/models/config.py
@@ -417,4 +417,5 @@ def verify_and_update_config(cls, vllm_config: "VllmConfig") -> None:
     "GptOssForCausalLM": GptOssForCausalLMConfig,
     "MambaForCausalLM": MambaModelConfig,
     "Mamba2ForCausalLM": MambaModelConfig,
+    "FalconMambaForCausalLM": MambaModelConfig,
 }