diff --git a/vllm/v1/attention/backends/flashinfer.py b/vllm/v1/attention/backends/flashinfer.py
index a79a7480b..7e272ab25 100755
--- a/vllm/v1/attention/backends/flashinfer.py
+++ b/vllm/v1/attention/backends/flashinfer.py
@@ -586,6 +586,7 @@ class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
         # try to use fp8 q if kv cache is fp8, and will fall back to model dtype
         # if TRTLLM attention kernel is not used when building attn metadata
         can_use_trtllm = can_use_trtllm_attention(self.num_qo_heads, self.num_kv_heads)
+
         if (
             can_use_trtllm
             and not vllm_config.attention_config.disable_flashinfer_q_quantization
@@ -1436,7 +1437,6 @@ class FlashInferImpl(AttentionImpl):
                 # This path needs to be enabled with VLLM_KV_CACHE_LAYOUT = HND
                 assert get_kv_cache_layout() == "HND"
                 assert is_strictly_contiguous(prefill_query)
-                assert is_strictly_contiguous(kv_cache_permute)
                 assert is_strictly_contiguous(workspace_buffer)
                 assert is_strictly_contiguous(block_tables_prefill)
                 assert is_strictly_contiguous(seq_lens_prefill)
@@ -1461,6 +1461,20 @@ class FlashInferImpl(AttentionImpl):
                     # and fp8 kv cache. So to enable prefill attention
                     # with fp8 kv cache, we can construct a mock block
                     # and mock kv cache with BF16 KV involved in the prefill
+                    #
+                    # The inner (block_size, head_size) dims must be
+                    # contiguous; outer dims may have non-canonical strides
+                    # (e.g. cross-layer unified allocation).
+                    # Degenerate strides on outer dims break TMA descriptors
+                    # (see flashinfer-ai/flashinfer#2232).
+                    kv_strides = kv_cache_permute.stride()
+                    assert (
+                        kv_strides[-1] == 1
+                        and kv_strides[-2] == kv_cache_permute.shape[-1]
+                    ), (
+                        "KV cache inner dims (block_size, head_size) must be "
+                        f"contiguous, got strides {kv_strides}"
+                    )
                     mock_kv_cache, mock_block_table = trtllm_prefill_attn_kvfp8_dequant(
                         kv_cache_permute,
                         block_tables_prefill,
@@ -1549,10 +1563,21 @@ class FlashInferImpl(AttentionImpl):
                 # This path needs to be enabled with VLLM_KV_CACHE_LAYOUT = HND
                 assert get_kv_cache_layout() == "HND"
                 assert is_strictly_contiguous(decode_query)
-                assert is_strictly_contiguous(kv_cache_permute)
                 assert is_strictly_contiguous(workspace_buffer)
                 assert is_strictly_contiguous(block_tables_decode)
                 assert is_strictly_contiguous(seq_lens_decode)
+                # kv_cache outer dims may be non-contiguous (e.g.
+                # cross-layer unified allocation), but inner dims
+                # (block_size, head_size) must be contiguous and
+                # strides must be canonical to avoid TMA descriptor
+                # failures (see flashinfer-ai/flashinfer#2232).
+                kv_strides = kv_cache_permute.stride()
+                assert (
+                    kv_strides[-1] == 1 and kv_strides[-2] == kv_cache_permute.shape[-1]
+                ), (
+                    "KV cache inner dims (block_size, head_size) must be "
+                    f"contiguous, got strides {kv_strides}"
+                )
 
                 if output.dtype == FP4_DTYPE:
                     assert self.o_sf_scale is not None