diff --git a/tests/v1/cudagraph/test_encoder_cudagraph.py b/tests/v1/cudagraph/test_encoder_cudagraph.py
index 3ad18cf9a7..a4d7b68544 100644
--- a/tests/v1/cudagraph/test_encoder_cudagraph.py
+++ b/tests/v1/cudagraph/test_encoder_cudagraph.py
@@ -32,6 +32,68 @@ from vllm.v1.worker.encoder_cudagraph_defs import (
 # ---------------------------------------------------------------------------
 
 
+class _MockCompilationConfig:
+    """Minimal mock for VllmConfig.compilation_config."""
+
+    def __init__(
+        self,
+        token_budgets: list[int] | None = None,
+        max_mm_items: int = 0,
+    ):
+        self.encoder_cudagraph_token_budgets = token_budgets or []
+        self.encoder_cudagraph_max_vision_items_per_batch = max_mm_items
+        self.encoder_cudagraph_max_frames_per_batch = None
+
+
+class _MockMultimodalConfig:
+    mm_encoder_tp_mode = "replicate"
+
+    def get_limit_per_prompt(self, modality: str) -> int:
+        # Image-only mocks — return 0 for "video" to short-circuit the
+        # max_frames_per_batch branch, so tests don't need a video-frame mock.
+        return 0
+
+
+class _MockModelConfig:
+    multimodal_config = _MockMultimodalConfig()
+
+
+class _MockParallelConfig:
+    tensor_parallel_size = 1
+
+
+class _MockVllmConfig:
+    """Minimal mock for VllmConfig used in __init__ tests."""
+
+    def __init__(
+        self,
+        token_budgets: list[int] | None = None,
+        max_mm_items: int = 0,
+    ):
+        self.compilation_config = _MockCompilationConfig(token_budgets, max_mm_items)
+        self.model_config = _MockModelConfig()
+        self.parallel_config = _MockParallelConfig()
+
+
+class _MockModel:
+    """Minimal mock implementing SupportsEncoderCudaGraph for __init__."""
+
+    def __init__(self, min_budget: int = 4, max_budget: int = 128):
+        self._min_budget = min_budget
+        self._max_budget = max_budget
+
+    def get_encoder_cudagraph_config(self) -> EncoderCudaGraphConfig:
+        return EncoderCudaGraphConfig(
+            modalities=["image"],
+            input_key_by_modality={"image": "pixel_values"},
+            buffer_keys=["dummy_buf"],
+            out_hidden_size=32,
+        )
+
+    def get_encoder_cudagraph_budget_range(self, vllm_config):
+        return (self._min_budget, self._max_budget)
+
+
 def _make_manager_with_budgets(budgets: list[int]) -> EncoderCudaGraphManager:
     """Create a minimal EncoderCudaGraphManager with only token_budgets set.
 
@@ -760,3 +822,113 @@ class TestEncoderCudaGraphVideoReplay:
         assert len(vid_result) == 1
         assert img_result[0].shape == (4, _HIDDEN)
         assert vid_result[0].shape == (8, _HIDDEN)
+
+
+# ---------------------------------------------------------------------------
+# __init__ invariant validation tests (no GPU required)
+# ---------------------------------------------------------------------------
+
+
+class TestInitInvariantValidation:
+    """Ensure max_batch_size <= min(token_budgets) for all config paths."""
+
+    def _make_mgr(
+        self,
+        token_budgets=None,
+        max_mm_items=0,
+        min_budget=4,
+        max_budget=128,
+    ):
+        vllm_config = _MockVllmConfig(token_budgets, max_mm_items)
+        model = _MockModel(min_budget, max_budget)
+        return EncoderCudaGraphManager(
+            vllm_config=vllm_config,
+            device=torch.device("cpu"),
+            dtype=torch.float32,
+            model=model,
+        )
+
+    # --- Finding 1: fully auto-inferred ---
+
+    def test_auto_inferred_invariant_holds(self):
+        mgr = self._make_mgr(min_budget=64, max_budget=16384)
+        assert mgr.max_batch_size <= min(mgr.token_budgets)
+
+    def test_auto_inferred_small_range(self):
+        mgr = self._make_mgr(min_budget=4, max_budget=128)
+        assert mgr.max_batch_size <= min(mgr.token_budgets)
+
+    # --- Finding 2: fully user-specified, bad combo ---
+
+    def test_user_specified_bad_combo_raises(self):
+        with pytest.raises(ValueError, match="must be <= smallest token budget"):
+            self._make_mgr(token_budgets=[64], max_mm_items=256)
+
+    def test_user_specified_valid_combo(self):
+        mgr = self._make_mgr(token_budgets=[64, 128], max_mm_items=32)
+        assert mgr.max_batch_size == 32
+        assert mgr.token_budgets == [64, 128]
+
+    def test_user_specified_exact_boundary(self):
+        # max_mm_items == min(budgets) is OK (per_image_output = 1)
+        mgr = self._make_mgr(token_budgets=[64, 128], max_mm_items=64)
+        assert mgr.max_batch_size == 64
+
+    # --- Finding 3: user provides only max_mm_items ---
+
+    def test_user_max_mm_items_only_adjusts_budgets(self):
+        # model min_budget=64, user max_mm_items=128 → budgets start at 128
+        mgr = self._make_mgr(max_mm_items=128, min_budget=64, max_budget=16384)
+        assert mgr.max_batch_size == 128
+        assert min(mgr.token_budgets) >= 128
+
+    def test_user_max_mm_items_smaller_than_min_budget(self):
+        # max_mm_items=2, model min=4 → budgets start at 4 (>= 2), OK
+        mgr = self._make_mgr(max_mm_items=2, min_budget=4, max_budget=128)
+        assert mgr.max_batch_size == 2
+        assert min(mgr.token_budgets) >= 2
+
+    # --- Finding 4: user provides only budgets ---
+
+    def test_user_budgets_only_caps_max_batch_size(self):
+        # user budgets start at 32, model min_budget=64
+        # without fix: max_batch_size = min(128//64, 64) = 2 → OK
+        # but if user budgets=[16, 64]:
+        # without fix: max_batch_size = min(128//4, 4) = 4 > 16? No.
+        # Let's use a case that triggers it:
+        # model min=64, max=16384 → max_budget//min_budget = 256
+        # user budgets=[32, 64] → min = 32
+        # without fix: max_batch_size = min(256, 64) = 64 > 32 → BUG
+        # with fix: max_batch_size = min(256, 32) = 32 → OK
+        mgr = self._make_mgr(token_budgets=[32, 64], min_budget=64, max_budget=16384)
+        assert mgr.max_batch_size <= min(mgr.token_budgets)
+        assert mgr.max_batch_size == 32
+
+    # --- Finding 5/6: bad model budget range ---
+
+    def test_zero_min_budget_raises(self):
+        with pytest.raises(ValueError, match="Both must be positive"):
+            self._make_mgr(min_budget=0, max_budget=128)
+
+    def test_negative_max_budget_raises(self):
+        with pytest.raises(ValueError, match="Both must be positive"):
+            self._make_mgr(min_budget=4, max_budget=-1)
+
+    def test_min_greater_than_max_raises(self):
+        with pytest.raises(ValueError, match="min_budget=200 > max_budget=100"):
+            self._make_mgr(min_budget=200, max_budget=100)
+
+    # --- Finding 7: user-provided budgets with non-positive values ---
+
+    def test_user_budgets_zero_raises(self):
+        """Non-positive budgets should be caught at config validation."""
+        from vllm.config.compilation import CompilationConfig
+
+        with pytest.raises(ValueError, match="must be positive"):
+            CompilationConfig(encoder_cudagraph_token_budgets=[0, 128])
+
+    def test_user_budgets_negative_raises(self):
+        from vllm.config.compilation import CompilationConfig
+
+        with pytest.raises(ValueError, match="must be positive"):
+            CompilationConfig(encoder_cudagraph_token_budgets=[-1, 64])
diff --git a/vllm/config/compilation.py b/vllm/config/compilation.py
index 350621c5d3..12d1b2c031 100644
--- a/vllm/config/compilation.py
+++ b/vllm/config/compilation.py
@@ -1014,6 +1014,14 @@ class CompilationConfig:
                 "non-negative (None = auto-infer)"
             )
 
+        if self.encoder_cudagraph_token_budgets and any(
+            b <= 0 for b in self.encoder_cudagraph_token_budgets
+        ):
+            raise ValueError(
+                f"All encoder_cudagraph_token_budgets must be positive, "
+                f"got {self.encoder_cudagraph_token_budgets}"
+            )
+
         if self.backend == "":
             self.backend = current_platform.get_compile_backend()
 
diff --git a/vllm/model_executor/models/qwen3_vl.py b/vllm/model_executor/models/qwen3_vl.py
index 9f2690eb3e..7df44dda59 100644
--- a/vllm/model_executor/models/qwen3_vl.py
+++ b/vllm/model_executor/models/qwen3_vl.py
@@ -1768,14 +1768,12 @@ class Qwen3VLForConditionalGeneration(
             EncoderCudaGraphConfig,
         )
 
-        modalities = ["image"]
-        # NOTE: When EVS (Efficient Video Sampling) pruning is enabled, the number
-        # of tokens becomes data-dependent (i.e., the retained tokens are
-        # dynamically selected based on inter-frame differences) and therefore
-        # cannot be captured by CUDA Graphs. As a result, video CUDA Graphs are
-        # only enabled when EVS is disabled.
-        if not self.is_multimodal_pruning_enabled:
-            modalities.append("video")
+        # When EVS pruning is enabled, embed_multimodal post-processes both
+        # image and video embeddings (mrope positions are appended for image,
+        # prune+append for video). The encoder CUDA graph path bypasses that
+        # post-process, producing inconsistent embedding formats vs eager. So
+        # disable CUDA graph for all modalities when pruning is on.
+        modalities = [] if self.is_multimodal_pruning_enabled else ["image", "video"]
 
         return EncoderCudaGraphConfig(
             modalities=modalities,
@@ -1923,7 +1921,10 @@ class Qwen3VLForConditionalGeneration(
         )
 
         spatial_merge_size = self.visual.spatial_merge_size
-        per_mm_item_output = token_budget // max_batch_size
+        # Ceil so the buffer fits the worst case of one item using the full
+        # budget. Floor under-allocates when budget is not a multiple of
+        # max_batch_size.
+        per_mm_item_output = (token_budget + max_batch_size - 1) // max_batch_size
 
         frames_per_item = max_frames_per_batch // max_batch_size
         if frames_per_item > 1:
diff --git a/vllm/v1/worker/encoder_cudagraph.py b/vllm/v1/worker/encoder_cudagraph.py
index bb805a5d6c..dd4d7262a7 100644
--- a/vllm/v1/worker/encoder_cudagraph.py
+++ b/vllm/v1/worker/encoder_cudagraph.py
@@ -72,25 +72,65 @@ class EncoderCudaGraphManager:
 
         multimodal_config = vllm_config.model_config.multimodal_config
 
+        # Invariant: max_batch_size <= min_token_budget.
+        # This ensures per_image_output = budget // max_batch_size >= 1
+        # for every captured budget, preventing reshape crashes on empty
+        # tensors during CUDA graph capture. Validated/enforced below for
+        # each configuration path.
         if user_budgets and user_max_vision_items > 0:
-            # Fully user-specified
+            # Fully user-specified: validate the invariant.
             self.token_budgets = sorted(user_budgets)
             self.max_batch_size = user_max_vision_items
+            min_tok = min(self.token_budgets)
+            if self.max_batch_size > min_tok:
+                raise ValueError(
+                    f"encoder_cudagraph_max_vision_items_per_batch "
+                    f"({self.max_batch_size}) must be <= smallest token "
+                    f"budget ({min_tok}). With budgets="
+                    f"{self.token_budgets}, per_image_output = "
+                    f"{min_tok} // {self.max_batch_size} = "
+                    f"{min_tok // self.max_batch_size}, which would cause "
+                    f"a capture failure. Either increase the smallest "
+                    f"budget or decrease max_vision_items_per_batch."
+                )
         else:
-            # Auto-infer missing values from model
+            # Auto-infer missing values from model.
             min_budget, max_budget = model.get_encoder_cudagraph_budget_range(
                 vllm_config
             )
-            self.token_budgets = (
-                sorted(user_budgets)
-                if user_budgets
-                else self._generate_budgets(min_budget, max_budget)
-            )
-            self.max_batch_size = (
-                user_max_vision_items
-                if user_max_vision_items > 0
-                else max_budget // min_budget
-            )
+            if min_budget <= 0 or max_budget <= 0:
+                raise ValueError(
+                    f"Invalid encoder cudagraph budget range: "
+                    f"min_budget={min_budget}, max_budget={max_budget}. "
+                    f"Both must be positive."
+                )
+            if min_budget > max_budget:
+                raise ValueError(
+                    f"Invalid encoder cudagraph budget range: "
+                    f"min_budget={min_budget} > max_budget={max_budget}."
+                )
+
+            if user_max_vision_items > 0:
+                # User provided max_vision_items only; adjust auto-inferred
+                # budgets so min(budgets) >= max_batch_size.
+                self.max_batch_size = user_max_vision_items
+                effective_min = max(min_budget, user_max_vision_items)
+                self.token_budgets = self._generate_budgets(effective_min, max_budget)
+            elif user_budgets:
+                # User provided budgets only; cap auto-inferred
+                # max_batch_size to min(user_budgets).
+                self.token_budgets = sorted(user_budgets)
+                self.max_batch_size = min(
+                    max_budget // min_budget,
+                    min(self.token_budgets),
+                )
+            else:
+                # Fully auto-inferred.
+                self.token_budgets = self._generate_budgets(min_budget, max_budget)
+                self.max_batch_size = min(
+                    max_budget // min_budget,
+                    min(self.token_budgets),
+                )
 
         assert multimodal_config is not None
         if multimodal_config.get_limit_per_prompt("video") == 0: