diff --git a/tests/models/registry.py b/tests/models/registry.py
index a5e83bc11f144..c80f045d98743 100644
--- a/tests/models/registry.py
+++ b/tests/models/registry.py
@@ -515,6 +515,9 @@ _MULTIMODAL_EXAMPLE_MODELS = {
                               trust_remote_code=True),
     "Llama_Nemotron_Nano_VL" : _HfExamplesInfo("nvidia/Llama-3.1-Nemotron-Nano-VL-8B-V1", # noqa: E501
                                                      trust_remote_code=True),
+    "NemotronH_Nano_VL": _HfExamplesInfo("nano_vl_dummy",
+                                          is_available_online=False,
+                                          trust_remote_code=True),
     "Ovis": _HfExamplesInfo("AIDC-AI/Ovis2-1B", trust_remote_code=True,
                             max_transformers_version="4.53",
                             transformers_version_reason="HF model is not compatible",  # noqa: E501
diff --git a/vllm/config/__init__.py b/vllm/config/__init__.py
index 7422527a6854b..4bab06a98cb21 100644
--- a/vllm/config/__init__.py
+++ b/vllm/config/__init__.py
@@ -1552,7 +1552,7 @@ class ModelConfig:
                        for bc in block_configs[start:end])
         else:
             # Hybrid model Jamba
-            layers_block_type_value = getattr(self.hf_config,
+            layers_block_type_value = getattr(self.hf_text_config,
                                               "layers_block_type", None)
             if layers_block_type_value is not None:
                 if hasattr(self.hf_text_config,
diff --git a/vllm/model_executor/models/nano_nemotron_vl.py b/vllm/model_executor/models/nano_nemotron_vl.py
new file mode 100644
index 0000000000000..21765a483b8e0
--- /dev/null
+++ b/vllm/model_executor/models/nano_nemotron_vl.py
@@ -0,0 +1,1395 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# --------------------------------------------------------
+# Adapted from
+# https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/internvl.py
+# under Apache-2.0 License
+#     LICENSE is in root directory.
+# --------------------------------------------------------
+
+import copy
+import warnings
+from abc import ABC, abstractmethod
+from collections.abc import Iterable, Mapping, Sequence
+from typing import Annotated, Any, Literal, Optional, TypedDict, TypeVar, Union
+
+import numpy.typing as npt
+import torch
+import torch.nn as nn
+import torchvision.transforms as T
+from PIL import Image
+from transformers import (AutoModel, BatchEncoding, BatchFeature,
+                          PretrainedConfig, TensorType)
+
+from vllm.config import VllmConfig
+from vllm.model_executor.layers.activation import ReLUSquaredActivation
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.interfaces import (HasInnerState, IsHybrid,
+                                                   MultiModalEmbeddings,
+                                                   SupportsMultiModal)
+from vllm.model_executor.models.internvl import (calculate_internvl_targets,
+                                                 get_internvl_target_ratios)
+from vllm.model_executor.models.module_mapping import MultiModelKeys
+from vllm.model_executor.models.nemotron_h import NemotronHForCausalLM
+from vllm.model_executor.models.utils import (flatten_bn,
+                                              init_vllm_registered_model,
+                                              maybe_prefix,
+                                              merge_multimodal_embeddings)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargs, MultiModalKwargsItems,
+                                    NestedTensors)
+from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
+                                   ImageSize, MultiModalDataItems)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, PromptReplacement,
+                                        PromptUpdate, PromptUpdateDetails)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
+
+# Configure PIL to handle large images without warnings
+# This prevents DecompressionBombWarning for legitimate large images
+Image.MAX_IMAGE_PIXELS = None  # Disable the limit entirely
+# Alternative: Set a specific higher limit
+# Image.MAX_IMAGE_PIXELS = 300000000  # ~300M pixels
+
+IMG_START = "<img>"
+IMG_END = "</img>"
+IMG_CONTEXT = "<image>"
+
+# Profiling
+MAX_FRAMES = 16
+
+
+class NanoNemotronVLImagePixelInputs(TypedDict):
+    type: Literal["pixel_values"]
+    pixel_values_flat: torch.Tensor
+    """
+    Shape:
+    `(batch_size * num_images * (1 + num_patches), num_channels, height, width)`
+    """
+
+    num_patches: torch.Tensor
+    """Shape: `(batch_size * num_images)`"""
+
+
+class NanoNemotronVLImageEmbeddinInputs(TypedDict):
+    type: Literal["image_embeds"]
+    data: Union[torch.Tensor, list[torch.Tensor]]
+    """ 
+    A tensor of shape `(num_images, total_image_feature_size, hidden_size)`
+    or a list of tensors of shape `(total_image_feature_size, hidden_size)`
+
+    `hidden_size` must match the hidden size of language model backbone.
+    """
+
+
+NanoNemotronVLImageInputs = Union[NanoNemotronVLImagePixelInputs,
+                                  NanoNemotronVLImageEmbeddinInputs]
+
+
+class NanoNemotronVLVideoPixelInputs(TensorSchema):
+    """
+    Dimensions:
+        - bvf: Batch size * number of videos * num_frames
+        - bn: Batch size * number of images
+        - c: Number of channels (3)
+        - h: Height of each video frame
+        - w: Width of each video frame
+    """
+    type: Literal["pixel_values_videos"]
+    pixel_values_flat: Annotated[torch.Tensor, TensorShape("bvf", 3, "h", "w")]
+    num_patches: Annotated[torch.Tensor, TensorShape("bn")]
+
+
+class NanoNemotronVLVideoEmbeddingInputs(TensorSchema):
+    """
+    Dimensions:
+        - n: Number of videos
+        - f: Total video feature size
+        - h: Hidden size (must match the hidden size of language model backbone)
+    """
+    type: Literal["video_embeds"]
+    data: Annotated[Union[torch.Tensor, list[torch.Tensor]],
+                    TensorShape("n", "f", "h")]
+
+
+NanoNemotronVLVideoInputs = Union[NanoNemotronVLVideoPixelInputs,
+                                  NanoNemotronVLVideoEmbeddingInputs]
+
+
+def input_conditioner(x, norm_mean, norm_std):
+    y = (x - norm_mean) / norm_std
+    return y
+
+
+def dynamic_preprocess(image,
+                       *,
+                       image_size=512,
+                       max_num_tiles=12,
+                       use_thumbnail=True,
+                       idx=0):
+    orig_width, orig_height = image.size
+
+    target_ratios = get_internvl_target_ratios(1, max_num_tiles)
+
+    blocks, target_width, target_height = calculate_internvl_targets(
+        orig_width=orig_width,
+        orig_height=orig_height,
+        target_ratios=target_ratios,
+        image_size=image_size,
+        use_thumbnail=False)
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size,
+        )
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+
+    processed_images = [
+        img.convert("RGB") if img.mode != "RGB" else img
+        for img in processed_images
+    ]
+    processed_images = [
+        T.Resize((image_size, image_size),
+                 interpolation=T.InterpolationMode.BICUBIC)(img)
+        for img in processed_images
+    ]
+    processed_images = [T.ToTensor()(img) for img in processed_images]
+    return processed_images
+
+
+def image_to_pixel_values(
+    image: Image.Image,
+    *,
+    input_size: int,
+    max_num: int,
+    use_thumbnail: bool,
+    idx: int,
+) -> torch.Tensor:
+    images = dynamic_preprocess(
+        image,
+        image_size=input_size,
+        max_num_tiles=max_num,
+        use_thumbnail=use_thumbnail,
+        idx=idx,
+    )
+
+    pixel_values = torch.stack(images)
+    return pixel_values
+
+
+def video_to_pixel_values(
+    video: npt.NDArray,
+    *,
+    input_size: int,
+    max_num_tiles: int = 1,
+    use_thumbnail: bool,
+) -> torch.Tensor:
+    # Convert each frame to a single resized tile tensor consistent
+    # with image path
+    frames_tensors: list[torch.Tensor] = []
+    for frame in video:
+        pil_frame = dynamic_preprocess(
+            Image.fromarray(frame, mode="RGB"),
+            image_size=input_size,
+            max_num_tiles=max_num_tiles,
+            use_thumbnail=use_thumbnail,
+            idx=0,
+        )
+        # dynamic_preprocess returns tensors already; take the single tile
+        assert len(pil_frame) >= 1
+        frames_tensors.append(pil_frame[0])
+
+    return torch.stack(frames_tensors)
+
+
+class BaseNanoNemotronVLProcessor(ABC):
+    """
+    This model doesn't define its own HF processor,
+    so we implement our own one here.
+
+    The code to insert image tokens is based on:
+    https://huggingface.co/OpenGVLab/InternVL2-1B/blob/main/modeling_internvl_chat.py#L252
+    """
+
+    def __init__(self, config: PretrainedConfig, tokenizer: AnyTokenizer,
+                 *args, **kwargs) -> None:
+        super().__init__()
+
+        self.config = config
+        self.tokenizer = tokenizer
+
+        image_size: int = config.force_image_size
+        patch_size: int = config.patch_size
+
+        self.num_image_token = int(
+            (image_size // patch_size)**2 * (config.downsample_ratio**2))
+        self.image_size = image_size
+        self.use_thumbnail: bool = config.use_thumbnail
+        self.norm_mean = torch.Tensor(config.norm_mean).reshape(1, 3, 1, 1)
+        self.norm_std = torch.Tensor(config.norm_std).reshape(1, 3, 1, 1)
+
+    @property
+    @abstractmethod
+    def image_token_id(self) -> int:
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_image_repl(
+        self,
+        feature_size: int,
+        num_patches: Optional[int],
+    ) -> PromptUpdateDetails[str]:
+        raise NotImplementedError
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        max_num_tiles: int,
+    ) -> int:
+        target_ratios = get_internvl_target_ratios(1, max_num_tiles)
+
+        num_patches, _, _ = calculate_internvl_targets(
+            orig_width=image_width,
+            orig_height=image_height,
+            target_ratios=target_ratios,
+            image_size=self.image_size,
+            use_thumbnail=self.use_thumbnail,
+        )
+
+        return num_patches * self.num_image_token
+
+    def _images_to_pixel_values_lst(
+        self,
+        images: list[Image.Image],
+        max_num_tiles: int,
+    ) -> list[torch.Tensor]:
+        return [
+            image_to_pixel_values(
+                image,
+                input_size=self.image_size,
+                max_num=max_num_tiles,
+                use_thumbnail=self.use_thumbnail,
+                idx=idx,
+            ) for idx, image in enumerate(images)
+        ]
+
+    def _preprocess_image(
+        self,
+        text: list[str],
+        images: list[Image.Image],
+        max_num_tiles: int,
+    ) -> tuple[list[str], dict[str, torch.Tensor]]:
+        if len(images) == 0:
+            image_inputs = {}
+        else:
+            pixel_values_lst = self._images_to_pixel_values_lst(
+                images, max_num_tiles)
+            image_inputs: dict[str, NestedTensors] = {
+                "pixel_values_flat":
+                input_conditioner(torch.cat(pixel_values_lst), self.norm_mean,
+                                  self.norm_std),
+                "image_num_patches":
+                torch.tensor([len(item) for item in pixel_values_lst]),
+            }
+
+            for pixel_values in pixel_values_lst:
+                num_patches = pixel_values.shape[0]
+                feature_size = num_patches * self.num_image_token
+                image_repl = self.get_image_repl(feature_size, num_patches)
+                text = [t.replace('<image>', image_repl.full, 1) for t in text]
+        return text, image_inputs
+
+    def _make_batch_input(self,
+                          input_item: Optional[Union[Any, list[Any]]] = None):
+        if input_item is None:
+            input_item = []
+        if not isinstance(input_item, list):
+            input_item = [input_item]
+        return input_item
+
+    def __call__(
+        self,
+        text: Optional[Union[str, list[str]]] = None,
+        images: Optional[Union[Image.Image, list[Image.Image]]] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        max_num_tiles: Optional[int] = None,
+    ) -> Mapping[str, NestedTensors]:
+        # Use default if not provided
+        if max_num_tiles is None:
+            max_num_tiles = 12
+
+        text, images = [self._make_batch_input(x) for x in (text, images)]
+
+        text, image_inputs = self._preprocess_image(
+            text=text,
+            images=images,
+            max_num_tiles=max_num_tiles,
+        )
+
+        text_inputs = self.tokenizer(text, add_special_tokens=False)
+
+        return {
+            **BatchEncoding(text_inputs, tensor_type=return_tensors),
+            **image_inputs,
+        }
+
+
+class NanoNemotronVLProcessor(BaseNanoNemotronVLProcessor):
+    """
+    HF Processor  with extended video processing logic.
+    Code for video processing is adapted from video example:
+    https://huggingface.co/OpenGVLab/InternVL3-1B#inference-with-transformers
+    """
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        tokenizer: AnyTokenizer,
+        *,
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+        video_token: Optional[str] = None,
+    ) -> None:
+        super().__init__(
+            config=config,
+            tokenizer=tokenizer,
+            min_dynamic_patch=min_dynamic_patch,
+            max_dynamic_patch=max_dynamic_patch,
+            dynamic_image_size=dynamic_image_size,
+        )
+        # add extra video token for video processing
+        self.video_token = video_token
+
+    @property
+    def supports_video(self) -> bool:
+        return self.video_token_id is not None
+
+    @property
+    def video_token_id(self) -> Optional[int]:
+        if self.video_token is None:
+            return None
+        return self.tokenizer.get_vocab().get(self.video_token, None)
+
+    @property
+    def image_token_id(self) -> int:
+        return self.tokenizer.convert_tokens_to_ids(IMG_CONTEXT)
+
+    def _videos_to_pixel_values_lst(
+        self,
+        videos: list[npt.NDArray],
+        max_num_tiles: int,
+        dynamic_image_size: Optional[bool] = None,
+    ) -> list[torch.Tensor]:
+
+        return [
+            video_to_pixel_values(
+                video,
+                input_size=self.image_size,
+                max_num_tiles=max_num_tiles,
+                use_thumbnail=self.use_thumbnail,
+            ) for video in videos
+        ]
+
+    def _preprocess_video(
+        self,
+        text: list[str],
+        videos: list[npt.NDArray],
+        max_num_tiles: int,
+        dynamic_image_size: Optional[bool] = None,
+    ):
+        if len(videos) == 0 or not self.supports_video:
+            video_inputs = {}
+        else:
+            pixel_values_lst_video = self._videos_to_pixel_values_lst(
+                videos,
+                max_num_tiles=max_num_tiles,
+                dynamic_image_size=dynamic_image_size,
+            )
+
+            video_inputs: dict[str, NestedTensors] = {
+                "pixel_values_flat_video":
+                input_conditioner(torch.cat(pixel_values_lst_video),
+                                  self.norm_mean, self.norm_std),
+                "video_num_patches":
+                torch.tensor([len(item) for item in pixel_values_lst_video]),
+            }
+
+            for pixel_values in pixel_values_lst_video:
+                num_patches = pixel_values.shape[0]
+
+                video_repl = self.get_video_repl(self.num_image_token,
+                                                 num_patches, self.video_token)
+                text = [t.replace('<video>', video_repl.full, 1) for t in text]
+        return text, video_inputs
+
+    def __call__(
+        self,
+        text: Optional[Union[str, list[str]]] = None,
+        images: Optional[Union[Image.Image, list[Image.Image]]] = None,
+        videos: Optional[Union[npt.NDArray, list[npt.NDArray]]] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        max_num_tiles: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+    ) -> Mapping[str, NestedTensors]:
+        # Use default if not provided
+        if max_num_tiles is None:
+            max_num_tiles = 12
+
+        text, images, videos = [
+            self._make_batch_input(x) for x in (text, images, videos)
+        ]
+
+        text, image_inputs = self._preprocess_image(
+            text=text,
+            images=images,
+            max_num_tiles=max_num_tiles,
+        )
+
+        text, video_inputs = self._preprocess_video(
+            text=text,
+            videos=videos,
+            max_num_tiles=max_num_tiles,
+            dynamic_image_size=dynamic_image_size,
+        )
+
+        text_inputs = self.tokenizer(text, add_special_tokens=False)
+
+        return BatchFeature({
+            **BatchEncoding(text_inputs, tensor_type=return_tensors),
+            **image_inputs,
+            **video_inputs,
+        })
+
+    def get_image_repl(
+        self,
+        feature_size: int,
+        num_patches: Optional[int],
+    ) -> PromptUpdateDetails[str]:
+        repl_features = IMG_CONTEXT * feature_size
+        repl_full = IMG_START + repl_features + IMG_END
+
+        return PromptUpdateDetails.select_text(repl_full, IMG_CONTEXT)
+
+    def get_video_repl(
+        self,
+        feature_size: int,
+        num_patches: Optional[int] = None,
+        video_context_token: str = IMG_CONTEXT,
+    ) -> PromptUpdateDetails[str]:
+        repl_features = video_context_token * self.num_image_token
+        repl_features_with_sep = IMG_START + repl_features + IMG_END
+        # num_patches is equal to num_frames
+        repl_full = ''.join([
+            f'Frame{i+1}: {repl_features_with_sep}' for i in range(num_patches)
+        ])
+
+        return PromptUpdateDetails.select_text(repl_full, video_context_token)
+
+
+class BaseNanoNemotronVLProcessingInfo(BaseProcessingInfo):
+    """Basic image-only ProcessingInfo for InternVL-style models."""
+
+    @abstractmethod
+    def get_hf_processor(
+        self,
+        **kwargs: object,
+    ) -> BaseNanoNemotronVLProcessor:
+        raise NotImplementedError
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None}
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        max_num_tiles: int,
+        processor: Optional[BaseNanoNemotronVLProcessor],
+    ) -> int:
+        if processor is None:
+            processor = self.get_hf_processor()
+
+        return processor.get_num_image_tokens(
+            image_width=image_width,
+            image_height=image_height,
+            max_num_tiles=max_num_tiles,
+        )
+
+    def get_image_size_with_most_features(self,
+                                          max_num_tiles: int) -> ImageSize:
+        processor = self.get_hf_processor()
+
+        base_size = processor.image_size
+        target_ratios = get_internvl_target_ratios(1, max_num_tiles)
+
+        largest_feature_size, largest_feature_pinpoint = 0, None
+        for wr, hr in target_ratios:
+            width, height = base_size * wr, base_size * hr
+
+            feat_size = self.get_num_image_tokens(
+                image_width=width,
+                image_height=height,
+                max_num_tiles=max_num_tiles,
+                processor=processor,
+            )
+            if feat_size > largest_feature_size:
+                largest_feature_size = feat_size
+                largest_feature_pinpoint = ImageSize(width=width,
+                                                     height=height)
+
+        if largest_feature_size == 0 or largest_feature_pinpoint is None:
+            raise ValueError("Cannot have a largest feature size of 0!")
+
+        return largest_feature_pinpoint
+
+    def get_max_image_tokens(self) -> int:
+        processor = self.get_hf_processor()
+        # Use default max_num_tiles for max tokens calculation
+        max_num_tiles = 12
+        target_width, target_height = self.get_image_size_with_most_features(
+            max_num_tiles)
+
+        return self.get_num_image_tokens(
+            image_width=target_width,
+            image_height=target_height,
+            max_num_tiles=max_num_tiles,
+            processor=processor,
+        )
+
+
+_I = TypeVar("_I", bound=BaseNanoNemotronVLProcessingInfo)
+
+
+class NanoNemotronVLProcessingInfo(BaseNanoNemotronVLProcessingInfo):
+    """ ProcessingInfo extended for video processing"""
+
+    @property
+    def supports_video(self):
+        return self.get_hf_processor().supports_video
+
+    def get_supported_mm_limits(self):
+        video_limit = {"video": None} if self.supports_video else {}
+        return {**super().get_supported_mm_limits(), **video_limit}
+
+    def get_video_token(self) -> Optional[str]:
+        return IMG_CONTEXT
+
+    def get_num_frames_with_most_features(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> int:
+        max_images = mm_counts.get("image", 0)
+        max_videos = mm_counts.get("video", 0)
+
+        processor = self.get_hf_processor()  # we get the CustomProcessor here
+
+        max_image_tokens = self.get_max_image_tokens() * max_images
+        max_total_frames = (seq_len -
+                            max_image_tokens) // processor.num_image_token
+        max_frames_per_video = max_total_frames // max(max_videos, 1)
+
+        max_frames_per_video = min(max_frames_per_video, MAX_FRAMES)
+        return max(max_frames_per_video, 1)
+
+    def get_hf_processor(self, **kwargs: object) -> NanoNemotronVLProcessor:
+        return self.ctx.init_processor(
+            NanoNemotronVLProcessor,
+            config=self.get_hf_config(),
+            tokenizer=self.get_tokenizer(),
+            video_token=self.get_video_token(),
+            **kwargs,
+        )
+
+
+class NanoNemotronBaseVLMultiModalProcessor(BaseMultiModalProcessor[_I]):
+    """Basic image-only MultiModalProcessor for InternVL-style models."""
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> Mapping[str, NestedTensors]:
+        processed_outputs = super()._call_hf_processor(
+            prompt=prompt,
+            mm_data=mm_data,
+            mm_kwargs=mm_kwargs,
+            tok_kwargs=tok_kwargs,
+        )
+
+        hf_processor = self.info.get_hf_processor(**mm_kwargs)
+        image_token_id = hf_processor.image_token_id
+
+        # Since there may be extra tokens in the feature placeholders,
+        # we need to pass the image token ID to the model to select the
+        # tokens to merge from the vision encoder outputs
+        processed_outputs["image_token_id"] = torch.tensor(image_token_id)
+
+        return processed_outputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: Mapping[str, NestedTensors],
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        image_num_patches = hf_inputs.get("image_num_patches", torch.empty(0))
+        num_images = len(image_num_patches)
+
+        return dict(
+            pixel_values_flat=MultiModalFieldConfig.flat_from_sizes(
+                "image", image_num_patches),
+            image_num_patches=MultiModalFieldConfig.batched("image"),
+            image_embeds=MultiModalFieldConfig.batched("image"),
+            image_token_id=MultiModalFieldConfig.shared("image", num_images),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+
+        if "image_num_patches" in out_mm_kwargs:
+            image_num_patches = out_mm_kwargs["image_num_patches"]
+            assert isinstance(image_num_patches, torch.Tensor)
+            image_num_patches = image_num_patches.tolist()
+        elif "image_embeds" in out_mm_kwargs:
+            # to compute num_patches (similar to Qwen2-VL)
+            image_num_patches = [None] * len(out_mm_kwargs["image_embeds"])
+        else:
+            image_num_patches = []
+
+        def get_replacement_custom(item_idx: int):
+            images = mm_items.get_items(
+                "image", (ImageEmbeddingItems, ImageProcessorItems))
+
+            if isinstance(images, ImageEmbeddingItems):
+                feature_size = images.get_feature_size(item_idx)
+            else:
+                image_size = images.get_image_size(item_idx)
+                # Extract max_num_tiles from kwargs, default to 12
+                max_num_tiles = hf_processor_mm_kwargs.get("max_num_tiles", 12)
+                feature_size = self.info.get_num_image_tokens(
+                    image_width=image_size.width,
+                    image_height=image_size.height,
+                    max_num_tiles=max_num_tiles,
+                    processor=hf_processor,
+                )
+
+            num_patches = None
+            local_image_num_patches = image_num_patches
+            if isinstance(local_image_num_patches, torch.Tensor):
+                local_image_num_patches = local_image_num_patches.tolist()
+            if isinstance(
+                    local_image_num_patches,
+                (list, tuple)) and item_idx < len(local_image_num_patches):
+                num_patches = int(local_image_num_patches[item_idx])
+
+            return hf_processor.get_image_repl(feature_size, num_patches)
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target="<image>",
+                replacement=get_replacement_custom,
+            )
+        ]
+
+
+class NanoNemotronVLMultiModalProcessor(
+        NanoNemotronBaseVLMultiModalProcessor[NanoNemotronVLProcessingInfo]):
+    """MultiModalProcessor extended for video support"""
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> Mapping[str, NestedTensors]:
+        processed_outputs = super()._call_hf_processor(prompt, mm_data,
+                                                       mm_kwargs, tok_kwargs)
+
+        hf_processor = self.info.get_hf_processor(**mm_kwargs)
+        if self.info.supports_video and (
+                video_token_id := hf_processor.video_token_id) is not None:
+            processed_outputs["video_token_id"] = torch.tensor(video_token_id)
+        return processed_outputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: Mapping[str, NestedTensors],
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        image_fields = super()._get_mm_fields_config(hf_inputs,
+                                                     hf_processor_mm_kwargs)
+        if self.info.supports_video:
+            video_num_patches = hf_inputs.get("video_num_patches",
+                                              torch.empty(0))
+            num_videos = len(video_num_patches)
+            video_fields = dict(
+                pixel_values_flat_video=MultiModalFieldConfig.flat_from_sizes(
+                    "video", video_num_patches),
+                video_num_patches=MultiModalFieldConfig.batched("video"),
+                video_token_id=MultiModalFieldConfig.shared(
+                    "video", num_videos))
+        else:
+            video_fields = {}
+
+        return image_fields | video_fields
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargsItems,
+    ) -> Sequence[PromptUpdate]:
+        prompt_repl = super()._get_prompt_updates(
+            mm_items=mm_items,
+            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
+            out_mm_kwargs=out_mm_kwargs,
+        )
+
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+
+        out_mm_data = out_mm_kwargs.get_data()
+        if "video_num_patches" in out_mm_data:
+            video_num_patches = out_mm_data["video_num_patches"]
+            assert isinstance(video_num_patches, torch.Tensor)
+            video_num_patches = video_num_patches.tolist()
+        else:
+            video_num_patches = []
+
+        def get_video_replacement_internvl(item_idx: int):
+            feature_size = hf_processor.num_image_token
+            num_patches = video_num_patches[item_idx]
+            if num_patches is not None:
+                assert isinstance(num_patches, int)
+
+            return hf_processor.get_video_repl(
+                feature_size,
+                num_patches,
+                video_context_token=hf_processor.video_token)
+
+        if self.info.supports_video:
+            prompt_repl = [
+                *prompt_repl,
+                PromptReplacement(
+                    modality="video",
+                    target="<video>",
+                    replacement=get_video_replacement_internvl,
+                )
+            ]
+
+        return prompt_repl
+
+
+class NanoNemotronVLDummyInputsBuilder(BaseDummyInputsBuilder[_I]):
+    """Basic image-only DummyInputsBuilder for InternVL-style models."""
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+
+        return "<image>" * num_images
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        # Use default max_num_tiles for dummy data generation
+        max_num_tiles = 12
+        target_width, target_height = (
+            self.info.get_image_size_with_most_features(max_num_tiles))
+        num_images = mm_counts.get("image", 0)
+
+        return {
+            "image":
+            self._get_dummy_images(width=target_width,
+                                   height=target_height,
+                                   num_images=num_images)
+        }
+
+
+class NanoNemotronVLDummyInputsBuilder(
+        NanoNemotronVLDummyInputsBuilder[NanoNemotronVLProcessingInfo]):
+    """DummyInputsBuilder extended for video support"""
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_videos = mm_counts.get("video", 0)
+
+        return super().get_dummy_text(mm_counts) + "<video>" * num_videos
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        dummy_image = super().get_dummy_mm_data(seq_len=seq_len,
+                                                mm_counts=mm_counts)
+        if self.info.supports_video:
+            config = self.info.get_hf_config()
+            image_size: int = config.force_image_size
+            target_num_frames = \
+                self.info.get_num_frames_with_most_features(seq_len, mm_counts)
+            num_videos = mm_counts.get("video", 0)
+            dummy_video = {
+                "video":
+                self._get_dummy_videos(width=image_size,
+                                       height=image_size,
+                                       num_frames=target_num_frames,
+                                       num_videos=num_videos)
+            }
+        else:
+            dummy_video = {}
+        return {**dummy_image, **dummy_video}
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    NanoNemotronVLMultiModalProcessor,
+    info=NanoNemotronVLProcessingInfo,
+    dummy_inputs=NanoNemotronVLDummyInputsBuilder,
+)
+class NemotronH_Nano_VL(nn.Module, HasInnerState, IsHybrid,
+                        SupportsMultiModal):
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return "<image>"
+        if modality.startswith("video"):
+            return "<video>"
+        return None
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+
+        image_size = config.force_image_size
+        patch_size = config.patch_size
+        self.patch_size = patch_size
+        self.template = config.template
+        self.num_image_token = int(
+            (image_size // patch_size)**2 * (config.downsample_ratio**2))
+        self.downsample_ratio = config.downsample_ratio
+        self.ps_version = config.ps_version
+        self.image_tag_type = config.image_tag_type
+
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
+        self.vision_model = AutoModel.from_config(config.vision_config,
+                                                  trust_remote_code=True)
+        self.vision_model.model._initialize_weights = (
+            self.vision_model.model._init_weights)
+        # Move input normalization to processor to mirror original HF
+        # implementation where normalization is done in fp32
+        self.vision_model.radio_model.make_preprocessor_external()
+        self.vision_model = self.vision_model.to(
+            self.language_model.config.torch_dtype)
+
+        self.drop_vision_class_token = True
+
+        # Construct the vision projection.
+        vit_hidden_size = config.vit_hidden_size
+        vision_projection_hidden_size = config.projector_hidden_size
+        llm_hidden_size = config.text_config.hidden_size
+
+        self.mlp1 = nn.Sequential(
+            RMSNorm(hidden_size=vit_hidden_size *
+                    int(1 / self.downsample_ratio)**2,
+                    eps=1e-5),
+            nn.Linear(
+                vit_hidden_size * int(1 / self.downsample_ratio)**2,
+                vision_projection_hidden_size,
+                bias=False,
+            ),
+            ReLUSquaredActivation(),
+            nn.Linear(vision_projection_hidden_size,
+                      llm_hidden_size,
+                      bias=False),
+        )
+        self.mlp1 = self.mlp1.to(self.language_model.config.torch_dtype)
+
+        self.img_context_token_id = None
+        self.video_context_token_id = None
+        self.config = config
+
+    def pixel_shuffle(self, x, scale_factor=0.5):
+        n, w, h, c = x.size()
+        # N, W, H, C --> N, W, H * scale, C // scale
+        x = x.view(
+            n,
+            w,
+            int(h * scale_factor),
+            int(c / scale_factor),
+        )
+        # N, W, H * scale, C // scale --> N, H * scale, W, C // scale
+        x = x.permute(0, 2, 1, 3).contiguous()
+        # N, H * scale, W, C // scale -->
+        # N, H * scale, W * scale, C // (scale ** 2)
+        x = x.view(
+            n,
+            int(h * scale_factor),
+            int(w * scale_factor),
+            int(c / (scale_factor * scale_factor)),
+        )
+        if self.ps_version == "v1":
+            warnings.warn(
+                "In ps_version 'v1', the height and width have not "
+                "been swapped back, which results in a transposed image.",
+                stacklevel=2,
+            )
+        else:
+            x = x.permute(0, 2, 1, 3).contiguous()
+        return x
+
+    def extract_feature(self, pixel_values):
+        vit_embeds = self.vision_model(pixel_values).features
+        vit_embeds = vit_embeds.to(dtype=torch.bfloat16)
+        h = w = int(vit_embeds.shape[1]**0.5)
+        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
+        vit_embeds = self.pixel_shuffle(vit_embeds,
+                                        scale_factor=self.downsample_ratio)
+        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1,
+                                        vit_embeds.shape[-1])
+        vit_embeds = self.mlp1(vit_embeds)
+        return vit_embeds
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[NanoNemotronVLImageInputs]:
+        pixel_values_flat = kwargs.pop("pixel_values_flat", None)
+        image_num_patches = kwargs.pop("image_num_patches", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+
+        if pixel_values_flat is None and image_embeds is None:
+            return None
+
+        if image_embeds is not None:
+            if not isinstance(image_embeds, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image embeddings. "
+                                 f"Got type: {type(image_embeds)}")
+
+            return NanoNemotronVLImageEmbeddinInputs(
+                type="image_embeds",
+                data=flatten_bn(image_embeds),
+            )
+
+        image_token_id = kwargs["image_token_id"]
+        assert isinstance(image_token_id, torch.Tensor)
+        self.img_context_token_id = image_token_id.flatten().unique().item()
+
+        if pixel_values_flat is not None:
+            if not isinstance(pixel_values_flat, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of pixel values. "
+                                 f"Got type: {type(pixel_values_flat)}")
+
+            if not isinstance(image_num_patches, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image_num_patches. "
+                                 f"Got type: {type(image_num_patches)}")
+
+            pixel_values_flat = flatten_bn(pixel_values_flat, concat=True)
+            image_num_patches = flatten_bn(image_num_patches, concat=True)
+
+            return NanoNemotronVLImagePixelInputs(
+                type="pixel_values",
+                pixel_values_flat=pixel_values_flat,
+                num_patches=image_num_patches,
+            )
+
+        raise AssertionError("This line should be unreachable.")
+
+    def _process_image_input(
+            self, image_input: NanoNemotronVLImageInputs) -> torch.Tensor:
+        if image_input["type"] == "image_embeds":
+            return image_input["data"]
+
+        assert self.vision_model is not None
+
+        image_embeds = self.extract_feature(image_input["pixel_values_flat"])
+        num_patches = image_input["num_patches"]
+
+        # Only one image in the current batch
+        if len(num_patches) == 1:
+            return (image_embeds.view(-1,
+                                      self.config.text_config.hidden_size), )
+
+        # NOTE: Image embeddings are split into separate tensors for each image
+        # by the size of each embedding.
+        feature_size = image_embeds.shape[1]
+        image_embeds = image_embeds.view(-1,
+                                         self.config.text_config.hidden_size)
+        image_feature_sizes = [
+            num_patches * feature_size for num_patches in num_patches
+        ]
+        return image_embeds.split(image_feature_sizes)
+
+    def _parse_and_validate_video_input(
+            self,
+            **kwargs: object) -> Optional[NanoNemotronVLVideoPixelInputs]:
+        pixel_values_flat_video = kwargs.pop("pixel_values_flat_video", None)
+        video_num_patches = kwargs.pop("video_num_patches", None)
+        video_embeds = kwargs.pop("video_embeds", None)
+
+        if pixel_values_flat_video is None and video_embeds is None:
+            return None
+
+        if video_embeds is not None:
+            return NanoNemotronVLVideoEmbeddingInputs(
+                type="video_embeds",
+                data=flatten_bn(video_embeds),
+            )
+
+        video_token_id = kwargs["video_token_id"]
+        assert isinstance(video_token_id, torch.Tensor)
+        self.video_context_token_id = video_token_id.flatten().unique().item()
+
+        if pixel_values_flat_video is not None:
+            if not isinstance(pixel_values_flat_video, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of pixel values. "
+                                 f"Got type: {type(pixel_values_flat_video)}")
+
+            if not isinstance(video_num_patches, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image_num_patches. "
+                                 f"Got type: {type(video_num_patches)}")
+
+            pixel_values_flat_video = flatten_bn(pixel_values_flat_video,
+                                                 concat=True)
+            video_num_patches = flatten_bn(video_num_patches, concat=True)
+            expected_h = expected_w = self.config.force_image_size
+            resolve_bindings = {"h": expected_h, "w": expected_w}
+
+            return NanoNemotronVLVideoPixelInputs(
+                type="pixel_values_videos",
+                pixel_values_flat=pixel_values_flat_video,
+                num_patches=video_num_patches,
+                resolve_bindings=resolve_bindings,
+            )
+
+        raise AssertionError("This line should be unreachable.")
+
+    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
+        modalities = {}
+        # Preserve the order of modalities if there are multiple of them
+        # from the order of kwargs.
+        for input_key in kwargs:
+            if input_key in ("pixel_values_flat",
+                             "image_embeds") and "images" not in modalities:
+                modalities["images"] = self._parse_and_validate_image_input(
+                    **kwargs)
+            if input_key in ("pixel_values_flat_video",
+                             ) and "videos" not in modalities:
+                modalities["videos"] = self._parse_and_validate_video_input(
+                    **kwargs)
+
+        return modalities
+
+    def get_multimodal_embeddings(
+            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+        # Validate the multimodal input keyword arguments
+        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
+        if modalities is None:
+            return []
+
+        # # The result multimodal_embeddings is tuple of tensors, with each
+        # tensor correspoending to a multimodal data item (image or video).
+        multimodal_embeddings: tuple[torch.Tensor, ...] = ()
+
+        # NOTE: It is important to iterate over the keys in this dictionary
+        # to preserve the order of the modalities.
+        for modality in modalities:
+            if modality == "images":
+                image_input = modalities["images"]
+                vision_embeddings = self._process_image_input(image_input)
+                multimodal_embeddings += vision_embeddings
+            if modality == "videos":
+                video_input = modalities["videos"]
+                video_embeddings = self._process_image_input(video_input)
+                multimodal_embeddings += video_embeddings
+
+        return multimodal_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+
+        if (multimodal_embeddings is not None
+                and len(multimodal_embeddings) != 0):
+            context_token_ids = [
+                token_id for token_id in (self.img_context_token_id,
+                                          self.video_context_token_id)
+                if token_id is not None
+            ]
+            assert len(context_token_ids) >= 1
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                multimodal_embeddings,
+                context_token_ids,
+            )
+
+        return inputs_embeds
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if intermediate_tensors is not None:
+            input_ids = None
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner, this
+        # condition is for v0 compatibility.
+        elif inputs_embeds is None:
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            input_ids = None
+
+        hidden_states = self.language_model(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+            **kwargs,
+        )
+
+        return hidden_states
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """
+        Get the module prefix in multimodal models
+        """
+        return MultiModelKeys.from_string_field(
+            language_model="language_model",
+            connector="mlp1",
+            tower_model="vision_model",
+        )
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+
+        def is_vision_model_weights(weight: tuple[str, torch.Tensor]):
+            return weight[0].startswith("vision_model")
+
+        def is_adapter_weights(weight: tuple[str, torch.Tensor]):
+            return weight[0].startswith("mlp1")
+
+        # Get references to parameters for direct loading
+        vision_model_dict = dict(self.vision_model.named_parameters())
+        vision_model_buffers = dict(self.vision_model.named_buffers())
+        adapter_dict = dict(self.mlp1.named_parameters())
+
+        def llm_weights_generator():
+            # Single pass over weights
+            for name, w in weights:
+                if is_vision_model_weights((name, w)):
+                    # Load vision encoder weights directly
+                    trimmed_name = ".".join(name.split(".")[1:])
+                    if "input_conditioner" in trimmed_name:
+                        continue
+                    if trimmed_name in vision_model_buffers:
+                        param = vision_model_buffers[trimmed_name]
+                    else:
+                        param = vision_model_dict[trimmed_name]
+                    with torch.no_grad():
+                        default_weight_loader(param, w)
+                elif is_adapter_weights((name, w)):
+                    # Load vision-language adapter weights directly
+                    trimmed_name = ".".join(name.split(".")[1:])
+                    param = adapter_dict[trimmed_name]
+                    with torch.no_grad():
+                        default_weight_loader(param, w)
+                else:
+                    # LLM weights: yield them to be loaded
+                    # by language_model.load_weights
+                    assert name.startswith("language_model")
+                    trimmed_name = ".".join(name.split(".")[1:])
+                    yield (trimmed_name, w)
+
+        # Now we call the language model load with the generator
+        self.language_model.load_weights(llm_weights_generator())
+
+    def print_architecture(self,
+                           detailed: bool = True,
+                           save_to_file: str = None):
+        """
+        Print model architecture with parameter names, shapes, and sizes.
+
+        Args:
+            detailed: If True, show detailed parameter breakdown
+            save_to_file: If provided, save output to this file path
+        """
+        import sys
+        from io import StringIO
+
+        # Capture output if saving to file
+        original_stdout = sys.stdout
+        if save_to_file:
+            sys.stdout = StringIO()
+
+        try:
+            print("=" * 100)
+            print("NemotronH_Nano_VL Model Architecture")
+            print("=" * 100)
+
+            total_params = 0
+            param_groups = {
+                "language_model": [],
+                "vision_model": [],
+                "mlp1": [],
+                "other": [],
+            }
+
+            for name, param in self.named_parameters():
+                param_size = param.numel()
+                total_params += param_size
+
+                # Group parameters by main component
+                if name.startswith("language_model"):
+                    param_groups["language_model"].append(
+                        (name, param.shape, param_size, param.dtype))
+                elif name.startswith("vision_model"):
+                    param_groups["vision_model"].append(
+                        (name, param.shape, param_size, param.dtype))
+                elif name.startswith("mlp1"):
+                    param_groups["mlp1"].append(
+                        (name, param.shape, param_size, param.dtype))
+                else:
+                    param_groups["other"].append(
+                        (name, param.shape, param_size, param.dtype))
+
+                if detailed:
+                    print(f"{name:<70} | Shape: {str(param.shape):<25} | "
+                          f"Size: {param_size:>12,} | Dtype: {param.dtype}")
+
+            print("=" * 100)
+            print("Summary by Component:")
+            print("-" * 60)
+
+            for component, params in param_groups.items():
+                if params:  # Only show components that have parameters
+                    component_total = sum(size for _, _, size, _ in params)
+                    percentage = ((component_total / total_params) *
+                                  100 if total_params > 0 else 0)
+                    print(f"{component:<20} | Parameters: {len(params):>4} | "
+                          f"Total Size: {component_total:>15,} | "
+                          f"{percentage:>6.2f}%")
+
+            print("-" * 60)
+            print(f"{'Total Parameters':<20} | {total_params:>15,}")
+
+            # Estimate memory usage (assuming bfloat16 = 2 bytes per parameter)
+            memory_mb = total_params * 2 / (1024**2)
+            memory_gb = memory_mb / 1024
+            print(f"{'Est. Memory (MB)':<20} | {memory_mb:>15.2f}")
+            print(f"{'Est. Memory (GB)':<20} | {memory_gb:>15.2f}")
+            print("=" * 100)
+
+            # Save to file if requested
+            if save_to_file:
+                output = sys.stdout.getvalue()
+                sys.stdout = original_stdout
+                with open(save_to_file, "w") as f:
+                    f.write(output)
+                print(f"Architecture saved to: {save_to_file}")
+                print(output)  # Also print to console
+
+        finally:
+            if save_to_file and sys.stdout != original_stdout:
+                sys.stdout = original_stdout
+
+    def get_model_info(self):
+        """
+        Get basic model information as a dictionary.
+        """
+        total_params = sum(p.numel() for p in self.parameters())
+
+        component_info = {}
+        for name, param in self.named_parameters():
+            component = name.split(".")[0]
+            if component not in component_info:
+                component_info[component] = {"params": 0, "size": 0}
+            component_info[component]["params"] += 1
+            component_info[component]["size"] += param.numel()
+
+        return {
+            "model_name": "NemotronH_Nano_VL",
+            "total_parameters": total_params,
+            "memory_estimate_mb": total_params * 2 / (1024**2),  # bfloat16
+            "components": component_info,
+            "config": {
+                "image_size": getattr(self.config, "force_image_size", None),
+                "patch_size": getattr(self.config, "patch_size", None),
+                "num_image_token": self.num_image_token,
+                "downsample_ratio": self.downsample_ratio,
+            },
+        }
+
+    def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
+        return self.language_model.mamba_cache.copy_inputs_before_cuda_graphs(
+            input_buffers, **kwargs)
+
+    def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
+        return (self.language_model.mamba_cache.
+                get_seqlen_agnostic_capture_inputs(batch_size))
+
+    @classmethod
+    def get_mamba_state_shape_from_config(cls, vllm_config: "VllmConfig"):
+        text_config = vllm_config.model_config.hf_config.text_config
+        temp_vllm_config = copy.deepcopy(vllm_config)
+        temp_vllm_config.model_config.hf_config = text_config
+        return NemotronHForCausalLM.get_mamba_state_shape_from_config(
+            temp_vllm_config)
+
+    @classmethod
+    def get_mamba_state_dtype_from_config(cls, vllm_config: "VllmConfig"):
+        text_config = vllm_config.model_config.hf_config.text_config
+        temp_vllm_config = copy.deepcopy(vllm_config)
+        temp_vllm_config.model_config.hf_config = text_config
+        return NemotronHForCausalLM.get_mamba_state_dtype_from_config(
+            temp_vllm_config)
diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py
index ed7797ce9d4f1..fbc369c870c57 100644
--- a/vllm/model_executor/models/registry.py
+++ b/vllm/model_executor/models/registry.py
@@ -223,6 +223,7 @@ _MULTIMODAL_MODELS = {
     "GraniteSpeechForConditionalGeneration": ("granite_speech", "GraniteSpeechForConditionalGeneration"),  # noqa: E501
     "H2OVLChatModel": ("h2ovl", "H2OVLChatModel"),
     "InternVLChatModel": ("internvl", "InternVLChatModel"),
+    "NemotronH_Nano_VL": ("nano_nemotron_vl", "NemotronH_Nano_VL"),
     "InternS1ForConditionalGeneration": ("interns1", "InternS1ForConditionalGeneration"),  # noqa: E501
     "InternVLForConditionalGeneration": ("interns1", "InternS1ForConditionalGeneration"),  # noqa: E501
     "Idefics3ForConditionalGeneration":("idefics3","Idefics3ForConditionalGeneration"),