diff --git a/vllm/config/compilation.py b/vllm/config/compilation.py
index 10673041aa68..088d0b1af757 100644
--- a/vllm/config/compilation.py
+++ b/vllm/config/compilation.py
@@ -18,6 +18,7 @@
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
 from vllm.utils.import_utils import resolve_obj_by_qualname
+from vllm.utils.math_utils import round_up
 from vllm.utils.torch_utils import is_torch_equal_or_newer
 
 if TYPE_CHECKING:
@@ -773,19 +774,8 @@ def post_init_cudagraph_sizes(self) -> None:
         if self.cudagraph_capture_sizes:
             assert self.cudagraph_capture_sizes[-1] == self.max_cudagraph_capture_size
 
-        # pre-compute the mapping from batch size to padded graph size
-        self.bs_to_padded_graph_size = [
-            0 for i in range(self.max_cudagraph_capture_size + 1)
-        ]
-        for end, start in zip(
-            self.cudagraph_capture_sizes + [self.max_cudagraph_capture_size + 1],
-            [0] + self.cudagraph_capture_sizes,
-        ):
-            for bs in range(start, end):
-                if bs == start:
-                    self.bs_to_padded_graph_size[bs] = start
-                else:
-                    self.bs_to_padded_graph_size[bs] = end
+        # May get recomputed in the model runner if adjustment is needed for spec-decode
+        self.compute_bs_to_padded_graph_size()
 
     def set_splitting_ops_for_v1(self):
         # NOTE: this function needs to be called only when mode is
@@ -922,3 +912,64 @@ def custom_op_log_check(self):
                     enable_str,
                     op,
                 )
+
+    def adjust_cudagraph_sizes_for_spec_decode(
+        self, uniform_decode_query_len: int, tensor_parallel_size: int
+    ):
+        multiple_of = uniform_decode_query_len
+        if tensor_parallel_size > 1:
+            multiple_of = max(uniform_decode_query_len, tensor_parallel_size)
+            if (
+                multiple_of % uniform_decode_query_len != 0
+                or multiple_of % tensor_parallel_size != 0
+            ):
+                raise ValueError(
+                    f"Can't determine cudagraph shapes that are both a "
+                    f"multiple of {uniform_decode_query_len} "
+                    f"(num_speculative_tokens + 1) required by spec-decode "
+                    f"and {tensor_parallel_size} (tensor_parallel_size) "
+                    f"required by sequence parallelism please adjust "
+                    f"num_speculative_tokens or disable sequence parallelism"
+                )
+
+        if not self.cudagraph_capture_sizes or multiple_of <= 1:
+            return
+
+        assert self.max_cudagraph_capture_size is not None
+        rounded_sizes = sorted(
+            set(
+                round_up(size, multiple_of)
+                for size in self.cudagraph_capture_sizes
+                if round_up(size, multiple_of) <= self.max_cudagraph_capture_size
+            )
+        )
+
+        if len(rounded_sizes) == 0:
+            logger.warning(
+                "No valid cudagraph sizes after rounding to multiple of "
+                " num_speculative_tokens + 1 (%d); please adjust num_speculative_tokens"
+                " or max_cudagraph_capture_size (or cudagraph_capture_sizes)",
+                multiple_of,
+            )
+            return
+
+        self.max_cudagraph_capture_size = rounded_sizes[-1]
+        self.cudagraph_capture_sizes = rounded_sizes
+
+        # Recompute after adjusting the cudagraph sizes
+        self.compute_bs_to_padded_graph_size()
+
+    def compute_bs_to_padded_graph_size(self):
+        # pre-compute the mapping from batch size to padded graph size
+        self.bs_to_padded_graph_size = [
+            0 for i in range(self.max_cudagraph_capture_size + 1)
+        ]
+        for end, start in zip(
+            self.cudagraph_capture_sizes + [self.max_cudagraph_capture_size + 1],
+            [0] + self.cudagraph_capture_sizes,
+        ):
+            for bs in range(start, end):
+                if bs == start:
+                    self.bs_to_padded_graph_size[bs] = start
+                else:
+                    self.bs_to_padded_graph_size[bs] = end
diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py
index 6590ca54af68..a87c40e3421d 100644
--- a/vllm/v1/worker/gpu_model_runner.py
+++ b/vllm/v1/worker/gpu_model_runner.py
@@ -4342,6 +4342,22 @@ def _check_and_update_cudagraph_mode(
                 "and make sure compilation mode is VLLM_COMPILE"
             )
 
+        # if we have dedicated decode cudagraphs, and spec-decode is enabled,
+        # we need to adjust the cudagraph sizes to be a multiple of the uniform
+        # decode query length to avoid: https://github.com/vllm-project/vllm/issues/28207
+        # temp-fix: https://github.com/vllm-project/vllm/issues/28207#issuecomment-3504004536
+        # Will be removed in the near future when we have seperate cudagraph capture
+        # sizes for decode and mixed prefill-decode.
+        if (
+            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
+            and cudagraph_mode.separate_routine()
+            and self.uniform_decode_query_len > 1
+        ):
+            self.compilation_config.adjust_cudagraph_sizes_for_spec_decode(
+                self.uniform_decode_query_len, self.parallel_config.tensor_parallel_size
+            )
+            self.cudagraph_batch_sizes = self.compilation_config.cudagraph_capture_sizes
+
         # Trigger cudagraph dispatching keys initialization after
         # resolved cudagraph mode.
         self.cudagraph_dispatcher.initialize_cudagraph_keys(