diff --git a/tests/v1/kv_connector/unit/test_nixl_connector.py b/tests/v1/kv_connector/unit/test_nixl_connector.py
index c5ca7df83685..0d39b96fd467 100644
--- a/tests/v1/kv_connector/unit/test_nixl_connector.py
+++ b/tests/v1/kv_connector/unit/test_nixl_connector.py
@@ -217,7 +217,7 @@ def _nixl_handshake(self, host: str, port: int, remote_tp_size: int,
         # These should've been done in register_kv_caches(), called by
         # gpu_model_runner. Here we just hardcode some dummy values.
         self.slot_size_bytes = 4096
-        self.block_len = self.slot_size_bytes * self.block_size
+        self.block_len = self.slot_size_bytes * self.block_size * 2
         self.num_blocks = 1
         self.dst_num_blocks[self.engine_id] = self.num_blocks
 
diff --git a/tests/v1/worker/test_gpu_model_runner.py b/tests/v1/worker/test_gpu_model_runner.py
index e151d388c293..2e4ab11c5922 100644
--- a/tests/v1/worker/test_gpu_model_runner.py
+++ b/tests/v1/worker/test_gpu_model_runner.py
@@ -99,6 +99,8 @@ def get_vllm_config():
 def model_runner():
     vllm_config = get_vllm_config()
     model_config = vllm_config.model_config
+    # set to bf16, otherwise FlexAttention is always chosen
+    torch.set_default_dtype(torch.bfloat16)
     num_heads = model_config.get_num_kv_heads(vllm_config.parallel_config)
     head_size = model_config.get_head_size()
     vllm_config.compilation_config.static_forward_context[
@@ -405,7 +407,7 @@ def test_kv_cache_stride_order(monkeypatch, model_runner):
     n_heads = model_runner.model_config.get_num_kv_heads(
         model_runner.parallel_config)
     expected_kv_cache_shape = [
-        2, NUM_BLOCKS, BLOCK_SIZE, n_heads,
+        NUM_BLOCKS, 2, BLOCK_SIZE, n_heads,
         model_runner.model_config.get_head_size()
     ]
     # TODO mla test
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py b/vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
index e7fc2b118145..b02caef31054 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
@@ -545,6 +545,7 @@ def __init__(self, vllm_config: VllmConfig, engine_id: str):
         self.backend_name = backend.get_name()
         attn_backend = backend_name_to_enum(self.backend_name)
         self._use_flashinfer = attn_backend == _Backend.FLASHINFER_VLLM_V1
+        self._use_flashattn = attn_backend == _Backend.FLASH_ATTN_VLLM_V1
         self._use_pallas_v1 = attn_backend == _Backend.PALLAS_VLLM_V1
         logger.debug("Detected attention backend %s", self.backend_name)
 
@@ -735,8 +736,8 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
                 self.slot_size_bytes = kv_elem_size * kv_latent_dim
             else:
                 # [2 (k and v), num_blocks, ...]
-                if self._use_flashinfer:
-                    # FlashInfer swaps 2<->num_blocks dimensions.
+                if self._use_flashinfer or self._use_flashattn:
+                    # FlashInfer and FlashAttn swaps 2<->num_blocks dimensions.
                     self.num_blocks = first_kv_cache.shape[0]
                     block_rank = 4  # [2, block_size, kv_heads, head_dim]
                 else:
@@ -772,13 +773,14 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
         # are non-contiguous (it's not locally guaranteed that they will be)
         # Disadvantage is that the encoded NixlAgentMetadata is now larger
         # (roughly 8KB vs 5KB).
-        # Conversely for FlashInfer, K and V are transferred in the same tensor
-        # to better exploit the memory layout (ie num_blocks is the first dim).
+        # Conversely for FlashInfer and FlashAttn, K and V are transferred
+        # in the same tensor to better exploit the memory layout (ie
+        # num_blocks is the first dim).
         for cache_or_caches in xfer_buffers.values():
             # Normalize to always be a list of caches
             cache_list = [cache_or_caches] if use_mla \
                          or self._use_pallas_v1 or self._use_flashinfer \
-                         else cache_or_caches
+                         or self._use_flashattn else cache_or_caches
             for cache in cache_list:
                 base_addr = cache.data_ptr()
                 region_len = self.num_blocks * self.block_len
@@ -935,8 +937,8 @@ def add_remote_agent(self,
         else:
             remote_block_size = nixl_agent_meta.block_len // (
                 self.slot_size_bytes * tp_ratio)
-            if self._use_flashinfer:
-                # Account for joint KV in FlashInfer.
+            if self._use_flashinfer or self._use_flashattn:
+                # Account for joint KV in FlashInfer and FlashAttn.
                 remote_block_size //= 2
 
             assert nixl_agent_meta.block_len == self.block_len * tp_ratio, (
diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/shared_storage_connector.py b/vllm/distributed/kv_transfer/kv_connector/v1/shared_storage_connector.py
index fd79387269d5..a1e42b0b07af 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/shared_storage_connector.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/shared_storage_connector.py
@@ -130,11 +130,14 @@ def inject_kv_into_layer(
                 dst_kv_cache_layer[slot_mapping, ...] = src_kv_cache
                 dst_kv_cache_layer.reshape(dst_kv_cache_layer_shape)
             else:
-                num_pages = dst_kv_cache_layer_shape[1]
+                num_pages = dst_kv_cache_layer_shape[0]
                 page_size = dst_kv_cache_layer_shape[2]
                 dst_kv_cache_layer = dst_kv_cache_layer.reshape(
-                    2, num_pages * page_size, -1)
-                dst_kv_cache_layer[:, slot_mapping, ...] = src_kv_cache
+                    num_pages, 2, page_size, -1)
+                slot_mapping_page = slot_mapping // page_size
+                slot_mapping_tok = slot_mapping % page_size
+                dst_kv_cache_layer[slot_mapping_page, :, slot_mapping_tok,
+                                   ...] = src_kv_cache
                 dst_kv_cache_layer.reshape(dst_kv_cache_layer_shape)
 
         # Get the metadata
@@ -216,9 +219,12 @@ def extract_kv_from_layer(
                 num_pages, page_size = layer.shape[0], layer.shape[1]
                 return layer.reshape(num_pages * page_size, -1)[slot_mapping,
                                                                 ...]
-            num_pages, page_size = layer.shape[1], layer.shape[2]
-            return layer.reshape(2, num_pages * page_size, -1)[:, slot_mapping,
-                                                               ...]
+            num_pages, page_size = layer.shape[0], layer.shape[2]
+            slot_mapping_page = slot_mapping // page_size
+            slot_mapping_tok = slot_mapping % page_size
+            return layer.reshape(num_pages, 2, page_size,
+                                 -1)[slot_mapping_page, :, slot_mapping_tok,
+                                     ...]
 
         connector_metadata = self._get_connector_metadata()
         assert isinstance(connector_metadata, SharedStorageConnectorMetadata)
diff --git a/vllm/v1/attention/backends/flash_attn.py b/vllm/v1/attention/backends/flash_attn.py
index 95ba56b35937..0a987f4f9f80 100755
--- a/vllm/v1/attention/backends/flash_attn.py
+++ b/vllm/v1/attention/backends/flash_attn.py
@@ -85,7 +85,7 @@ def get_kv_cache_shape(
     ) -> tuple[int, ...]:
         if block_size % 16 != 0:
             raise ValueError("Block size must be a multiple of 16.")
-        return (2, num_blocks, block_size, num_kv_heads, head_size)
+        return (num_blocks, 2, block_size, num_kv_heads, head_size)
 
     @staticmethod
     def get_kv_cache_stride_order() -> tuple[int, ...]:
@@ -436,7 +436,7 @@ def forward(
             query: shape = [num_tokens, num_heads, head_size]
             key: shape = [num_tokens, num_kv_heads, head_size]
             value: shape = [num_tokens, num_kv_heads, head_size]
-            kv_cache = [2, num_blocks, block_size, num_kv_heads, head_size]
+            kv_cache = [num_blocks, 2, block_size, num_kv_heads, head_size]
             attn_metadata: Metadata for attention.
         Returns:
             shape = [num_tokens, num_heads * head_size]
@@ -479,7 +479,7 @@ def forward(
                                                    attn_metadata, layer)
 
         # For decoder and cross-attention, use KV cache as before
-        key_cache, value_cache = kv_cache.unbind(0)
+        key_cache, value_cache = kv_cache.unbind(1)
 
         if self.kv_sharing_target_layer_name is None:
             # Reshape the input keys and values and store them in the cache.