diff --git a/colossalai/kernel/kernel_loader.py b/colossalai/kernel/kernel_loader.py
index 2411b6482..36a49aae9 100644
--- a/colossalai/kernel/kernel_loader.py
+++ b/colossalai/kernel/kernel_loader.py
@@ -119,6 +119,10 @@ class FlashAttentionLoader(KernelLoader):
     ]
 
 
+class FlashAttentionDaoLoader(KernelLoader):
+    REGISTRY = [FlashAttentionDaoCudaExtension]
+
+
 class FlashAttentionWithCustomMaskLoader(KernelLoader):
     REGISTRY = [FlashAttentionNpuExtension, FlashAttentionSdpaCudaExtension]
 
diff --git a/colossalai/shardformer/layer/attn.py b/colossalai/shardformer/layer/attn.py
index bf4fa77c6..2f8e4d677 100644
--- a/colossalai/shardformer/layer/attn.py
+++ b/colossalai/shardformer/layer/attn.py
@@ -8,6 +8,7 @@ import torch.nn.functional as F
 from einops import rearrange
 
 from colossalai.kernel.kernel_loader import (
+    FlashAttentionDaoLoader,
     FlashAttentionForFloatAndCustomMaskLoader,
     FlashAttentionLoader,
     FlashAttentionWithCustomMaskLoader,
@@ -17,6 +18,8 @@ from colossalai.logging import get_dist_logger
 
 from .utils import RingComm, get_half_index, split_varlen_zigzag
 
+MEMORY_BOUND = 10 * 1e9
+
 __all__ = [
     "AttnMaskType",
     "ColoAttention",
@@ -77,6 +80,7 @@ def get_pad_info(
 
 class ColoAttention:
     _kernel_dispatch_map: Optional[Dict[torch.dtype, Dict[Optional[AttnMaskType], Callable]]] = None
+    _flash_kernel_dispatch: Optional[Dict[torch.dtype, Dict[Optional[AttnMaskType], Callable]]] = None
 
     @staticmethod
     def _init_kernels_dispatch():
@@ -102,9 +106,11 @@ class ColoAttention:
                 torch.bfloat16: half_dispatch_map,
                 torch.float32: float_dispatch_map,
             }
+        if ColoAttention._flash_kernel_dispatch is None:
+            ColoAttention._flash_kernel_dispatch = FlashAttentionDaoLoader()
 
     @staticmethod
-    def _dispatch_kernel(dtype: torch.dtype, mask_type: Optional[AttnMaskType]) -> Callable:
+    def _dispatch_kernel(dtype: torch.dtype, mask_type: Optional[AttnMaskType], size) -> Callable:
         ColoAttention._init_kernels_dispatch()
         if (
             dtype not in ColoAttention._kernel_dispatch_map
@@ -113,12 +119,19 @@ class ColoAttention:
             raise ValueError(
                 "FlashAttention kernel is not available for dtype {} and mask_type {}".format(dtype, mask_type)
             )
+
+        if size >= MEMORY_BOUND:
+            ColoAttention._flash_kernel_dispatch = ColoAttention._flash_kernel_dispatch.load()
         # lazy load
         if isinstance(ColoAttention._kernel_dispatch_map[dtype][mask_type], KernelLoader):
             ColoAttention._kernel_dispatch_map[dtype][mask_type] = ColoAttention._kernel_dispatch_map[dtype][
                 mask_type
             ].load()
-        return ColoAttention._kernel_dispatch_map[dtype][mask_type]
+
+        if size >= MEMORY_BOUND and mask_type in (AttnMaskType.PADDED_CAUSAL, AttnMaskType.CAUSAL):
+            return ColoAttention._flash_kernel_dispatch
+        else:
+            return ColoAttention._kernel_dispatch_map[dtype][mask_type]
 
     @staticmethod
     def prepare_attn_kwargs(
@@ -154,6 +167,8 @@ class ColoAttention:
             return {}
         assert len(shape_4d) == 4 and shape_4d[1] == 1
         b, _, s_q, s_kv = shape_4d
+        element_size = torch.tensor([], dtype=dtype).element_size()
+        memory_size = s_q * s_kv * element_size
         outputs = {}
         if (q_padding_mask is None or q_padding_mask.bool().all()) and (
             kv_padding_mask is None or kv_padding_mask.bool().all()
@@ -161,10 +176,13 @@ class ColoAttention:
             # no padding
             assert is_causal
             outputs["attention_mask_type"] = AttnMaskType.CAUSAL
-            attention_mask = torch.ones(s_q, s_kv, dtype=dtype, device=device)
-            if s_q != 1:
-                attention_mask = attention_mask.tril(diagonal=0)
-            attention_mask = attention_mask.expand(b, s_q, s_kv)
+            if memory_size < MEMORY_BOUND:
+                attention_mask = torch.ones(s_q, s_kv, dtype=dtype, device=device)
+                if s_q != 1:
+                    attention_mask.tril_(diagonal=0)
+                attention_mask = attention_mask.expand(b, s_q, s_kv)
+            else:
+                attention_mask = torch.empty((0,), dtype=dtype, device=device)
         else:
             max_seqlen_q, cu_seqlens_q, q_indices = get_pad_info(q_padding_mask)
             if kv_padding_mask is None:
@@ -177,7 +195,6 @@ class ColoAttention:
                 b,
                 s_kv,
             ), f"Padding mask shape {kv_padding_mask.shape} should align with shape 4d ({b}, {s_kv})"
-            attention_mask = kv_padding_mask[:, None, :].expand(b, s_q, s_kv).to(dtype=dtype, device=device)
             outputs.update(
                 {
                     "cu_seqlens_q": cu_seqlens_q,
@@ -190,10 +207,17 @@ class ColoAttention:
             )
             if is_causal:
                 outputs["attention_mask_type"] = AttnMaskType.PADDED_CAUSAL
-                if s_q != 1:
-                    attention_mask = attention_mask * attention_mask.new_ones(s_q, s_kv).tril(diagonal=0)
+                if memory_size < MEMORY_BOUND:
+                    if s_q != 1:
+                        attention_mask = kv_padding_mask[:, None, :].expand(b, s_q, s_kv).to(dtype=dtype, device=device)
+                        attention_mask = attention_mask * attention_mask.new_ones(s_q, s_kv).tril(diagonal=0)
+                else:
+                    attention_mask = torch.empty((0,), dtype=dtype, device=device)
             else:
                 outputs["attention_mask_type"] = AttnMaskType.PADDED
+                if memory_size < MEMORY_BOUND:
+                    attention_mask = kv_padding_mask[:, None, :].expand(b, s_q, s_kv).to(dtype=dtype, device=device)
+
         if invert:
             attention_mask = invert_mask(attention_mask).unsqueeze(1)
         outputs["attention_mask"] = attention_mask
@@ -278,8 +302,12 @@ class ColoAttention:
             assert attention_mask_type == AttnMaskType.CUSTOM
 
         # kernel dispatch
+        b, _, s_q, _ = q.shape
+        b, _, s_kv, _ = v.shape
+        element_size = torch.tensor([], dtype=q.dtype).element_size()
+        memory_size = s_q * s_kv * element_size
         mask_type = attention_mask_type if attention_mask is not None else None
-        attn_func = ColoAttention._dispatch_kernel(q.dtype, mask_type)
+        attn_func = ColoAttention._dispatch_kernel(q.dtype, mask_type, memory_size)
         is_causal = attention_mask is not None and attention_mask_type in (
             AttnMaskType.CAUSAL,
             AttnMaskType.PADDED_CAUSAL,