diff --git a/vllm/v1/worker/tpu_worker.py b/vllm/v1/worker/tpu_worker.py
index bd24072f4c1a..67902b41b284 100644
--- a/vllm/v1/worker/tpu_worker.py
+++ b/vllm/v1/worker/tpu_worker.py
@@ -18,7 +18,7 @@
 from vllm.model_executor import set_random_seed
 from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE
 from vllm.v1.core.sched.output import SchedulerOutput
-from vllm.v1.kv_cache_interface import (FullAttentionSpec, KVCacheConfig,
+from vllm.v1.kv_cache_interface import (AttentionSpec, KVCacheConfig,
                                         KVCacheSpec)
 from vllm.v1.outputs import ModelRunnerOutput
 from vllm.v1.utils import bind_kv_cache
@@ -137,7 +137,7 @@ def determine_available_memory(self) -> int:
         kv_caches: dict[str, torch.Tensor] = {}
         kv_cache_spec = self.model_runner.get_kv_cache_spec()
         for layer_name, layer_spec in kv_cache_spec.items():
-            if isinstance(layer_spec, FullAttentionSpec):
+            if isinstance(layer_spec, AttentionSpec):
                 dtype = layer_spec.dtype
 
                 # Use an empty tensor instead of `None`` to force Dynamo to pass
@@ -147,7 +147,8 @@ def determine_available_memory(self) -> int:
                                             device=self.device)
                 kv_caches[layer_name] = tpu_kv_cache
             else:
-                raise NotImplementedError
+                raise NotImplementedError(
+                    f"Unsupported KV cache spec '{type(layer_spec)}'")
 
         runner_kv_caches: list[torch.Tensor] = []
         bind_kv_cache(