diff --git a/python/sglang/srt/model_executor/cuda_graph_runner.py b/python/sglang/srt/model_executor/cuda_graph_runner.py
index 702fb34da..6501b6fe1 100644
--- a/python/sglang/srt/model_executor/cuda_graph_runner.py
+++ b/python/sglang/srt/model_executor/cuda_graph_runner.py
@@ -530,18 +530,6 @@ def set_global_graph_memory_pool(val):
     global_graph_memory_pool = val
 
 
-def _ci_use_ascending_capture_order(server_args) -> bool:
-    """Whether CI + FA3 forces ascending cuda-graph capture (FA3 varlen IMA workaround, #26532)."""
-    if not envs.SGLANG_IS_IN_CI.get():
-        return False
-    prefill_backend, decode_backend = server_args.get_attention_backends()
-    return "fa3" in (
-        prefill_backend,
-        decode_backend,
-        server_args.speculative_draft_attention_backend,
-    )
-
-
 class CudaGraphRunner:
     """A CudaGraphRunner runs the forward pass of a model with cuda graph and torch.compile."""
 
@@ -833,17 +821,11 @@ class CudaGraphRunner:
                 self.model_runner.gpu_id,
                 empty_cache=False,
             )
-            # Reverse for memory sharing; CI+FA3 uses ascending to dodge the
-            # FA3 varlen workspace-slot IMA (#26532).
-            bs_seq = (
-                list(self.capture_bs)
-                if _ci_use_ascending_capture_order(self.model_runner.server_args)
-                else list(reversed(self.capture_bs))
-            )
+            # Reverse the order to enable better memory sharing across cuda graphs.
             capture_range = (
-                tqdm.tqdm(bs_seq)
+                tqdm.tqdm(list(reversed(self.capture_bs)))
                 if get_tensor_model_parallel_rank() == 0
-                else iter(bs_seq)
+                else reversed(self.capture_bs)
             )
             for i, bs in enumerate(capture_range):
                 if get_tensor_model_parallel_rank() == 0: