diff --git a/vllm/attention/backends/abstract.py b/vllm/attention/backends/abstract.py
index d28bc065852db..188becb6ad6f0 100644
--- a/vllm/attention/backends/abstract.py
+++ b/vllm/attention/backends/abstract.py
@@ -119,14 +119,12 @@ class AttentionBackend(ABC):
             return True
 
         for supported_size in cls.supported_kernel_block_sizes:
-            is_multiple_of = (
-                isinstance(supported_size, MultipleOf)
-                and block_size % supported_size.base == 0
-            )
-            is_int_equal = (
-                isinstance(supported_size, int) and block_size == supported_size
-            )
-            if is_multiple_of or is_int_equal:
+            if isinstance(supported_size, MultipleOf):
+                supported_size = supported_size.base
+            # With hybrid_blocks feature, the framework-level block size
+            # only needs to be a multiple of the kernel's requirement,
+            # even if the kernel requires a fixed block_size.
+            if block_size % supported_size == 0:
                 return True
         return False
 
diff --git a/vllm/v1/attention/backends/mla/rocm_aiter_mla.py b/vllm/v1/attention/backends/mla/rocm_aiter_mla.py
index e1864526f02cc..6ccc1a341d56c 100644
--- a/vllm/v1/attention/backends/mla/rocm_aiter_mla.py
+++ b/vllm/v1/attention/backends/mla/rocm_aiter_mla.py
@@ -7,9 +7,8 @@ from typing import ClassVar
 import torch
 
 from vllm._aiter_ops import rocm_aiter_ops
-from vllm.attention.backends.abstract import AttentionLayer
+from vllm.attention.backends.abstract import AttentionLayer, MultipleOf
 from vllm.config import VllmConfig
-from vllm.utils.math_utils import cdiv
 from vllm.v1.attention.backends.mla.common import (
     MLACommonBackend,
     MLACommonDecodeMetadata,
@@ -22,6 +21,8 @@ from vllm.v1.kv_cache_interface import AttentionSpec
 
 
 class AiterMLABackend(MLACommonBackend):
+    supported_kernel_block_sizes: ClassVar[list[int | MultipleOf]] = [1]
+
     @staticmethod
     def get_name() -> str:
         return "ROCM_AITER_MLA"
@@ -71,9 +72,8 @@ class AiterMLAMetadataBuilder(MLACommonMetadataBuilder[AiterMLAMetadata]):
         )
 
         self.compilation_config = vllm_config.compilation_config
-        max_num_pages_per_req = cdiv(
-            vllm_config.model_config.max_model_len, self.kv_cache_spec.block_size
-        )
+        # kernel block size is always 1.
+        max_num_pages_per_req = vllm_config.model_config.max_model_len
         max_num_reqs = vllm_config.scheduler_config.max_num_seqs
         max_num_pages = max_num_reqs * max_num_pages_per_req
 
@@ -82,11 +82,6 @@ class AiterMLAMetadataBuilder(MLACommonMetadataBuilder[AiterMLAMetadata]):
         # so we can only use the persistent buffer if a cudagraph is actually
         # being used.
         if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
-            self.block_table_remapping = torch.zeros(
-                [max_num_reqs, max_num_pages_per_req * self.kv_cache_spec.block_size],
-                dtype=torch.int32,
-                device=device,
-            )
             self.paged_kv_indptr = torch.zeros(
                 max_num_reqs + 1, dtype=torch.int32, device=device
             )
@@ -111,36 +106,16 @@ class AiterMLAMetadataBuilder(MLACommonMetadataBuilder[AiterMLAMetadata]):
         num_decode_tokens: int,
         dcp_tot_seq_lens_device: torch.Tensor | None,
     ) -> AiterMLADecodeMetadata:
-        page_size = self.kv_cache_spec.block_size
+        # kernel block size is always 1, although the kv block size is not 1.
         device = self.device
         num_reqs = seq_lens_device.size(0)
-        bs, _ = block_table_tensor.shape
-        block_table_tensor = (
-            block_table_tensor.unsqueeze(-1).expand(-1, -1, page_size) * page_size
-        )
-        block_table_tensor = (
-            block_table_tensor
-            + torch.arange(
-                0,
-                page_size,
-                device=block_table_tensor.device,
-                dtype=block_table_tensor.dtype,
-            )[None, None, :]
-        )
-        block_table_tensor = block_table_tensor.view(bs, -1)
 
-        # after remapping, we assume the block size already equals to 1
-
-        max_blk_size_per_req = block_table_tensor.shape[-1]
         mask = torch.arange(
             block_table_tensor.size(1), dtype=block_table_tensor.dtype, device=device
         ).unsqueeze(0) < seq_lens_device.unsqueeze(1)
         paged_kv_indices = block_table_tensor[mask]
 
-        paged_kv_last_page_len = seq_lens_device % page_size
-        paged_kv_last_page_len = torch.where(
-            paged_kv_last_page_len == 0, page_size, paged_kv_last_page_len
-        )
+        paged_kv_last_page_len = torch.where(seq_lens_device == 0, 1, seq_lens_device)
 
         paged_kv_indptr = torch.cat(
             [
@@ -151,12 +126,6 @@ class AiterMLAMetadataBuilder(MLACommonMetadataBuilder[AiterMLAMetadata]):
 
         if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
             num_actual_pages = paged_kv_indices.size(0)
-            self.block_table_remapping[:num_reqs, :max_blk_size_per_req].copy_(
-                block_table_tensor, non_blocking=True
-            )
-            block_table_tensor = self.block_table_remapping[
-                :num_reqs, :max_blk_size_per_req
-            ]
 
             self.paged_kv_indices[:num_actual_pages].copy_(
                 paged_kv_indices, non_blocking=True