diff --git a/tests/v1/tpu/test_sampler.py b/tests/v1/tpu/test_sampler.py
index 74ad81406b4..50d40aa9dec 100644
--- a/tests/v1/tpu/test_sampler.py
+++ b/tests/v1/tpu/test_sampler.py
@@ -42,7 +42,7 @@ def test_sampler_different(model_name: str):
         sampling_params = SamplingParams(temperature=0.3, seed=42)
         output2 = llm.generate(prompts, sampling_params)
 
-    # Batch-case with TopK
+    # Batch-case with TopK/P
     for B in [4, 16]:
         p = prompts * B
         sampling_params = [
@@ -51,9 +51,10 @@ def test_sampler_different(model_name: str):
                 min_p=0.8,
                 max_tokens=64,
                 # Vary number of ks
-                top_k=random.randint(4, 12)) for _ in range(B)
+                top_k=random.randint(4, 12),
+                top_p=random.random()) for _ in range(B)
         ]
-        # Make sure first two reqs have the same K
+        # Make sure first two reqs have the same K/P
         sampling_params[0] = sampling_params[1]
         output = llm.generate(p, sampling_params)
         assert output[0].outputs[0].text == output[1].outputs[0].text
diff --git a/vllm/v1/sample/tpu/metadata.py b/vllm/v1/sample/tpu/metadata.py
index 917d8baf60b..d4ea8c2dee0 100644
--- a/vllm/v1/sample/tpu/metadata.py
+++ b/vllm/v1/sample/tpu/metadata.py
@@ -11,7 +11,7 @@
     min_p=0.0,
     # strictly disabled for now
     top_k=0,
-    # top_p=0.0,
+    top_p=1.0,
     # frequency_penalties=0.0,
     # presence_penalties=0.0,
     # repetition_penalties=0.0,
@@ -26,11 +26,9 @@ class TPUSupportedSamplingMetadata:
     temperature: torch.Tensor = None
 
     min_p: torch.Tensor = None
-    # Still too slow on forward_native!
     top_k: torch.Tensor = None
     top_p: torch.Tensor = None
 
-    # Greedy sampling flag for compiling single xla graph.
     all_greedy: bool = True
 
     # unsupported, you need to return an extra tensor of static size BxV
@@ -103,9 +101,8 @@ def fill_slice(cpu_tensor: torch.Tensor, fill_val) -> torch.Tensor:
                    DEFAULT_SAMPLING_PARAMS["min_p"])
         fill_slice(input_batch.top_k_cpu_tensor,
                    DEFAULT_SAMPLING_PARAMS["top_k"])
-        # TODO Temporarily disabled until sampling options are enabled
-        # fill_slice(input_batch.top_p_cpu_tensor,
-        #            DEFAULT_SAMPLING_PARAMS["top_p"])
+        fill_slice(input_batch.top_p_cpu_tensor,
+                   DEFAULT_SAMPLING_PARAMS["top_p"])
 
         # Slice persistent device tensors to a fixed pre-compiled padded shape.
         return cls(
@@ -113,7 +110,8 @@ def fill_slice(cpu_tensor: torch.Tensor, fill_val) -> torch.Tensor:
             to(xla_device),
             all_greedy=input_batch.all_greedy,
             # TODO enable more and avoid returning None values
-            top_p=None,  # input_batch.top_p[:padded_num_reqs],
+            top_p=input_batch.top_p_cpu_tensor[:padded_num_reqs].to(
+                xla_device),
             top_k=input_batch.top_k_cpu_tensor[:padded_num_reqs].to(
                 xla_device),
             min_p=input_batch.min_p_cpu_tensor[:padded_num_reqs].to(