diff --git a/requirements/tpu.txt b/requirements/tpu.txt
index 3b204a8f9905..edc8b2a45667 100644
--- a/requirements/tpu.txt
+++ b/requirements/tpu.txt
@@ -18,9 +18,9 @@ setuptools==78.1.0
 --find-links https://storage.googleapis.com/libtpu-releases/index.html
 --find-links https://storage.googleapis.com/jax-releases/jax_nightly_releases.html
 --find-links https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
-torch==2.8.0.dev20250518
-torchvision==0.22.0.dev20250518
-torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.8.0.dev20250518-cp39-cp39-linux_x86_64.whl ; python_version == "3.9"
-torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.8.0.dev20250518-cp310-cp310-linux_x86_64.whl ; python_version == "3.10"
-torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.8.0.dev20250518-cp311-cp311-linux_x86_64.whl ; python_version == "3.11"
+torch==2.8.0.dev20250529
+torchvision==0.22.0.dev20250529
+torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.8.0.dev20250529-cp39-cp39-linux_x86_64.whl ; python_version == "3.9"
+torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.8.0.dev20250529-cp310-cp310-linux_x86_64.whl ; python_version == "3.10"
+torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.8.0.dev20250529-cp311-cp311-linux_x86_64.whl ; python_version == "3.11"
 
diff --git a/tests/tpu/test_moe_pallas.py b/tests/tpu/test_moe_pallas.py
index 13fc8bc8fa2e..19df22f78039 100644
--- a/tests/tpu/test_moe_pallas.py
+++ b/tests/tpu/test_moe_pallas.py
@@ -26,7 +26,7 @@ TOP_KS = [2, 6]
 # The Pallas GMM kernel requires num_tokens * topk to be a multiple of 16
 @pytest.mark.parametrize("m", [8, 16, 64, 2048])
 @pytest.mark.parametrize("n", [128, 1024, 2048])
-@pytest.mark.parametrize("k", [128, 511, 1024])
+@pytest.mark.parametrize("k", [128, 512, 1024])
 @pytest.mark.parametrize("e", NUM_EXPERTS)
 @pytest.mark.parametrize("topk", TOP_KS)
 @pytest.mark.parametrize("ep_size", EP_SIZE)
diff --git a/vllm/model_executor/layers/fused_moe/moe_pallas.py b/vllm/model_executor/layers/fused_moe/moe_pallas.py
index 53945999288d..9d8bd62c6969 100644
--- a/vllm/model_executor/layers/fused_moe/moe_pallas.py
+++ b/vllm/model_executor/layers/fused_moe/moe_pallas.py
@@ -67,15 +67,10 @@ def fused_moe(
     token_indices = token_indices[topk_argsort_indices]
     group_sizes = _histogram(topk_indices.to(torch.int32), 0, num_experts - 1)
 
-    # NOTE(woosuk): The GMM Pallas kernel requires a different weight layout
-    # from HF Transformers.
-    w1 = w1.transpose(1, 2)
-    w2 = w2.transpose(1, 2)
-
     x = hidden_states[token_indices]
-    x = torch.ops.xla.gmm(x, w1, group_sizes)
+    x = torch.ops.xla.gmm(x, w1, group_sizes, transpose_rhs=True)
     x = F.silu(x[..., :intermediate_size]) * x[..., intermediate_size:]
-    x = torch.ops.xla.gmm(x, w2, group_sizes)
+    x = torch.ops.xla.gmm(x, w2, group_sizes, transpose_rhs=True)
     x = x[topk_argsort_revert_indices].reshape(-1, topk, hidden_size)
 
     x = x * topk_weights.unsqueeze(dim=-1)