diff --git a/vllm/worker/model_runner.py b/vllm/worker/model_runner.py
index 9db3261b8ac36..f88b1d84fbcd1 100644
--- a/vllm/worker/model_runner.py
+++ b/vllm/worker/model_runner.py
@@ -17,6 +17,7 @@
 import vllm.envs as envs
 from vllm.attention import AttentionMetadata, get_attn_backend
 from vllm.attention.backends.abstract import AttentionState
+from vllm.attention.backends.utils import CommonAttentionState
 from vllm.compilation.compile_context import set_compile_context
 from vllm.compilation.levels import CompilationLevel
 from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
@@ -1001,6 +1002,17 @@ def __init__(
         self.graph_block_tables = np.zeros(
             (self.max_batchsize_to_capture, self.get_max_block_per_batch()),
             dtype=np.int32)
+
+        # Attention-free but stateful models like Mamba need a placeholder attn
+        # backend, as the attention metadata is needed to manage internal state.
+        # However we must bypass attention selection altogether for some models
+        # used for speculative decoding to avoid a divide-by-zero in
+        # model_config.get_head_size()
+        num_attn_heads = self.model_config.get_num_attention_heads(
+            self.parallel_config)
+        needs_attn_backend = (num_attn_heads != 0
+                              or self.model_config.is_attention_free)
+
         self.attn_backend = get_attn_backend(
             self.model_config.get_head_size(),
             self.model_config.get_sliding_window(),
@@ -1008,9 +1020,12 @@ def __init__(
             self.kv_cache_dtype,
             self.block_size,
             self.model_config.is_attention_free,
-        )
-        self.attn_state = self.attn_backend.get_state_cls()(
-            weakref.proxy(self))
+        ) if needs_attn_backend else None
+        if self.attn_backend:
+            self.attn_state = self.attn_backend.get_state_cls()(
+                weakref.proxy(self))
+        else:
+            self.attn_state = CommonAttentionState(weakref.proxy(self))
 
         # Multi-modal data support
         self.input_registry = input_registry