diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py
index 91015ad4379c..03b14ef80a5d 100644
--- a/vllm/v1/worker/gpu_model_runner.py
+++ b/vllm/v1/worker/gpu_model_runner.py
@@ -136,6 +136,7 @@
 from vllm.v1.worker.ubatch_utils import (
     UBatchSlice,
     UBatchSlices,
+    check_cudagraph_threshold,
     check_ubatch_thresholds,
 )
 from vllm.v1.worker.utils import is_residual_scattered_for_sp
@@ -3980,7 +3981,7 @@ def _capture_cudagraphs(
             # cudagraph, a uniform decode batch, and the number of tokens
             # is above the threshold. Otherwise we just capture a non-ubatched
             # version of the graph
-            allow_microbatching = (
+            microbatching_enabled = (
                 self.parallel_config.enable_dbo
                 and cudagraph_runtime_mode == CUDAGraphMode.FULL
                 and uniform_decode
@@ -3991,32 +3992,45 @@ def _capture_cudagraphs(
                 )
             )
 
-            for _ in range(self.compilation_config.cudagraph_num_of_warmups):
-                # Use CUDAGraphRuntimeStyle.NONE (default) for warmup.
-                # But be careful, warm up with `NONE`is orthogonal to
-                # if we want to warm up attention or not. This is
-                # different from the case where `FULL` implies capture
-                # attention while `PIECEWISE` implies no attention.
-                force_attention = cudagraph_runtime_mode == CUDAGraphMode.FULL
+            # When num_tokens is near the dbo_decode_token_threshold, different ranks
+            # may make different microbatching decisions (some above threshold, some
+            # below). Since all ranks must agree for DBO to work, they'll all fall back
+            # to non-DBO execution. To avoid running without cudagraphs in these mixed
+            # cases, we preemptively compile cudagraphs for both microbatching modes.
+            microbatching_modes = [microbatching_enabled]
+            compile_both_modes = check_cudagraph_threshold(
+                self.parallel_config, num_tokens, uniform_decode
+            )
+            if microbatching_enabled and compile_both_modes:
+                microbatching_modes = [False, True]  # Compile both modes
+
+            for allow_microbatching in microbatching_modes:
+                for _ in range(self.compilation_config.cudagraph_num_of_warmups):
+                    # Use CUDAGraphRuntimeStyle.NONE (default) for warmup.
+                    # But be careful, warm up with `NONE`is orthogonal to
+                    # if we want to warm up attention or not. This is
+                    # different from the case where `FULL` implies capture
+                    # attention while `PIECEWISE` implies no attention.
+                    force_attention = cudagraph_runtime_mode == CUDAGraphMode.FULL
+                    self._dummy_run(
+                        num_tokens,
+                        cudagraph_runtime_mode=CUDAGraphMode.NONE,
+                        force_attention=force_attention,
+                        uniform_decode=uniform_decode,
+                        allow_microbatching=allow_microbatching,
+                        skip_eplb=True,
+                        remove_lora=False,
+                        activate_lora=activate_lora,
+                    )
                 self._dummy_run(
                     num_tokens,
-                    cudagraph_runtime_mode=CUDAGraphMode.NONE,
-                    force_attention=force_attention,
+                    cudagraph_runtime_mode=cudagraph_runtime_mode,
                     uniform_decode=uniform_decode,
                     allow_microbatching=allow_microbatching,
                     skip_eplb=True,
                     remove_lora=False,
                     activate_lora=activate_lora,
                 )
-            self._dummy_run(
-                num_tokens,
-                cudagraph_runtime_mode=cudagraph_runtime_mode,
-                uniform_decode=uniform_decode,
-                allow_microbatching=allow_microbatching,
-                skip_eplb=True,
-                remove_lora=False,
-                activate_lora=activate_lora,
-            )
         self.maybe_remove_all_loras(self.lora_config)
 
     def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
diff --git a/vllm/v1/worker/gpu_ubatch_wrapper.py b/vllm/v1/worker/gpu_ubatch_wrapper.py
index 9de123263755..7614c21eaf3e 100644
--- a/vllm/v1/worker/gpu_ubatch_wrapper.py
+++ b/vllm/v1/worker/gpu_ubatch_wrapper.py
@@ -23,6 +23,7 @@
 from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors
 from vllm.utils.import_utils import has_deep_gemm
+from vllm.v1.worker.ubatch_utils import check_cudagraph_threshold
 from vllm.v1.worker.ubatching import UBatchContext, make_ubatch_contexts
 
 logger = init_logger(__name__)
@@ -388,7 +389,13 @@ def __call__(self, *args, **kwargs):
             # for this shape during a normal run.
             if cudagraph_runtime_mode is CUDAGraphMode.FULL:
                 assert batch_descriptor is not None
-                if batch_descriptor.num_tokens in self.cudagraphs:
+                num_tokens = batch_descriptor.num_tokens
+                uniform_decode = batch_descriptor.uniform_decode
+                # Check if the model runner made a non-dbo cudagraph for this shape
+                cudagraph_exists = check_cudagraph_threshold(
+                    self.vllm_config.parallel_config, num_tokens, uniform_decode
+                )
+                if num_tokens in self.cudagraphs and not cudagraph_exists:
                     cudagraph_runtime_mode = CUDAGraphMode.NONE
 
             if cudagraph_runtime_mode in (CUDAGraphMode.NONE, CUDAGraphMode.PIECEWISE):
diff --git a/vllm/v1/worker/ubatch_utils.py b/vllm/v1/worker/ubatch_utils.py
index 33a1921d2d98..fce91e532c25 100644
--- a/vllm/v1/worker/ubatch_utils.py
+++ b/vllm/v1/worker/ubatch_utils.py
@@ -42,6 +42,12 @@ def check_ubatch_thresholds(
         return num_tokens >= config.dbo_prefill_token_threshold
 
 
+def check_cudagraph_threshold(
+    config: ParallelConfig, num_tokens: int, uniform_decode: bool
+):
+    return uniform_decode and num_tokens <= config.dbo_decode_token_threshold * 1.5
+
+
 def create_ubatch_slices(
     num_scheduled_tokens: np.ndarray, split_point: int
 ) -> UBatchSlices: