diff --git a/vllm/v1/sample/ops/topk_topp_sampler.py b/vllm/v1/sample/ops/topk_topp_sampler.py
index dcae8f974..33f7090e4 100644
--- a/vllm/v1/sample/ops/topk_topp_sampler.py
+++ b/vllm/v1/sample/ops/topk_topp_sampler.py
@@ -248,7 +248,7 @@ def apply_top_k_top_p(
     if p is None and k is None:
         return logits
 
-    if HAS_TRITON and logits.shape[0] >= 8 and logits.is_cuda:
+    if HAS_TRITON and logits.shape[0] >= 8:
         return apply_top_k_top_p_triton(logits, k, p)
 
     # Use pytorch sort implementation for small batch sizes.
diff --git a/vllm/v1/sample/ops/topk_topp_triton.py b/vllm/v1/sample/ops/topk_topp_triton.py
index f776e94d6..f0291978d 100644
--- a/vllm/v1/sample/ops/topk_topp_triton.py
+++ b/vllm/v1/sample/ops/topk_topp_triton.py
@@ -967,7 +967,6 @@ def apply_top_k_top_p_triton(
     """
     assert logits.ndim == 2
     assert logits.dtype == torch.float32
-    assert logits.is_cuda
 
     batch_size, vocab_size = logits.shape
 
@@ -978,13 +977,13 @@ def apply_top_k_top_p_triton(
         return logits
 
     if k is not None:
-        assert k.ndim == 1 and k.shape[0] == batch_size and k.is_cuda
+        assert k.ndim == 1 and k.shape[0] == batch_size
         k_ptr = k.to(torch.int32)
     else:
         k_ptr = logits  # Dummy pointer (won't be read)
 
     if p is not None:
-        assert p.ndim == 1 and p.shape[0] == batch_size and p.is_cuda
+        assert p.ndim == 1 and p.shape[0] == batch_size
         p_ptr = p.to(torch.float32)
     else:
         p_ptr = logits  # Dummy pointer (won't be read)