diff --git a/tests/models/multimodal/generation/test_whisper.py b/tests/models/multimodal/generation/test_whisper.py
index 8c99b6b4690a9..592862c2a0bb0 100644
--- a/tests/models/multimodal/generation/test_whisper.py
+++ b/tests/models/multimodal/generation/test_whisper.py
@@ -103,6 +103,8 @@ def run_test(
         max_model_len=448,
         tensor_parallel_size=tensor_parallel_size,
         distributed_executor_backend=distributed_executor_backend,
+        # TODO (NickLucche) figure out output differences with non-eager and re-enable
+        enforce_eager=True,
     ) as vllm_model:
         llm = vllm_model.llm
 
diff --git a/vllm/config/vllm.py b/vllm/config/vllm.py
index 8f27db0013305..607bb44cddd26 100644
--- a/vllm/config/vllm.py
+++ b/vllm/config/vllm.py
@@ -666,8 +666,9 @@ class VllmConfig:
 
         default_config = OPTIMIZATION_LEVEL_TO_CONFIG[self.optimization_level]
         self._apply_optimization_level_defaults(default_config)
+
         if (
-            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
+            self.compilation_config.cudagraph_mode.requires_piecewise_compilation()
             and self.compilation_config.mode != CompilationMode.VLLM_COMPILE
         ):
             logger.info(
@@ -692,22 +693,29 @@ class VllmConfig:
 
         if current_platform.support_static_graph_mode():
             # if cudagraph_mode has full cudagraphs, we need to check support
-            if (
-                self.compilation_config.cudagraph_mode.has_full_cudagraphs()
-                and self.model_config is not None
-            ):
-                if self.model_config.pooler_config is not None:
+            if model_config := self.model_config:
+                if (
+                    self.compilation_config.cudagraph_mode.has_full_cudagraphs()
+                    and model_config.pooler_config is not None
+                ):
                     logger.warning_once(
                         "Pooling models do not support full cudagraphs. "
                         "Overriding cudagraph_mode to PIECEWISE."
                     )
                     self.compilation_config.cudagraph_mode = CUDAGraphMode.PIECEWISE
-                elif self.model_config.is_encoder_decoder:
-                    logger.warning_once(
-                        "Encoder-decoder models do not support full cudagraphs. "
-                        "Overriding cudagraph_mode to PIECEWISE."
+                elif (
+                    model_config.is_encoder_decoder
+                    and self.compilation_config.cudagraph_mode
+                    not in (CUDAGraphMode.NONE, CUDAGraphMode.FULL_DECODE_ONLY)
+                ):
+                    logger.info_once(
+                        "Encoder-decoder models do not support %s. "
+                        "Overriding cudagraph_mode to FULL_DECODE_ONLY.",
+                        self.compilation_config.cudagraph_mode.name,
+                    )
+                    self.compilation_config.cudagraph_mode = (
+                        CUDAGraphMode.FULL_DECODE_ONLY
                     )
-                    self.compilation_config.cudagraph_mode = CUDAGraphMode.PIECEWISE
 
             # disable cudagraph when enforce eager execution
             if self.model_config is not None and self.model_config.enforce_eager:
diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py
index 39456d2e80ed0..ca06f048f290b 100644
--- a/vllm/v1/worker/gpu_model_runner.py
+++ b/vllm/v1/worker/gpu_model_runner.py
@@ -1267,6 +1267,8 @@ class GPUModelRunner(
         if not isinstance(kv_cache_spec, CrossAttentionSpec):
             return None, None
 
+        # Zero out buffer for padding requests that are not actually scheduled (CGs)
+        self.encoder_seq_lens.np[:num_reqs] = 0
         # Build encoder_seq_lens array mapping request indices to
         # encoder lengths for inputs scheduled in this batch
         for req_id in num_scheduled_tokens:
@@ -2764,6 +2766,7 @@ class GPUModelRunner(
         # be improved in model runner v2)
         force_uniform_decode: bool | None = None,
         force_has_lora: bool | None = None,
+        num_encoder_reqs: int = 0,
     ) -> tuple[
         CUDAGraphMode,
         BatchDescriptor,
@@ -2780,6 +2783,11 @@ class GPUModelRunner(
             if force_uniform_decode is None
             else force_uniform_decode
         )
+        # Encoder-decoder models only support CG for decoder_step > 0 (no enc_output
+        # is present). Also, chunked-prefill is disabled, so batch are uniform.
+        has_encoder_output = (
+            self.model_config.is_encoder_decoder and num_encoder_reqs > 0
+        )
 
         has_lora = (
             len(self.input_batch.lora_id_to_lora_request) > 0
@@ -2799,7 +2807,7 @@ class GPUModelRunner(
         )
 
         cudagraph_mode, batch_descriptor = dispatch_cudagraph(
-            num_tokens_padded, use_cascade_attn
+            num_tokens_padded, use_cascade_attn or has_encoder_output
         )
         num_tokens_padded = batch_descriptor.num_tokens
 
@@ -2997,6 +3005,7 @@ class GPUModelRunner(
                     num_scheduled_tokens_np=num_scheduled_tokens_np,
                     max_num_scheduled_tokens=max_num_scheduled_tokens,
                     use_cascade_attn=cascade_attn_prefix_lens is not None,
+                    num_encoder_reqs=len(scheduler_output.scheduled_encoder_inputs),
                 )
 
                 logger.debug(