diff --git a/keras/src/backend/__init__.py b/keras/src/backend/__init__.py
index c724ce72631..35df39b5c84 100644
--- a/keras/src/backend/__init__.py
+++ b/keras/src/backend/__init__.py
@@ -44,6 +44,10 @@
 elif backend() == "numpy":
     from keras.src.backend.numpy import *  # noqa: F403
 
+    distribution_lib = None
+elif backend() == "openvino":
+    from keras.src.backend.openvino import *  # noqa: F403
+
     distribution_lib = None
 else:
     raise ValueError(f"Unable to import backend : {backend()}")
diff --git a/keras/src/backend/openvino/__init__.py b/keras/src/backend/openvino/__init__.py
new file mode 100644
index 00000000000..d9148e0a049
--- /dev/null
+++ b/keras/src/backend/openvino/__init__.py
@@ -0,0 +1,24 @@
+from keras.src.backend.common.name_scope import name_scope
+from keras.src.backend.openvino import core
+from keras.src.backend.openvino import image
+from keras.src.backend.openvino import linalg
+from keras.src.backend.openvino import math
+from keras.src.backend.openvino import nn
+from keras.src.backend.openvino import numpy
+from keras.src.backend.openvino import random
+from keras.src.backend.openvino.core import IS_THREAD_SAFE
+from keras.src.backend.openvino.core import SUPPORTS_SPARSE_TENSORS
+from keras.src.backend.openvino.core import Variable
+from keras.src.backend.openvino.core import cast
+from keras.src.backend.openvino.core import compute_output_spec
+from keras.src.backend.openvino.core import cond
+from keras.src.backend.openvino.core import convert_to_numpy
+from keras.src.backend.openvino.core import convert_to_tensor
+from keras.src.backend.openvino.core import is_tensor
+from keras.src.backend.openvino.core import random_seed_dtype
+from keras.src.backend.openvino.core import shape
+from keras.src.backend.openvino.core import vectorized_map
+from keras.src.backend.openvino.rnn import cudnn_ok
+from keras.src.backend.openvino.rnn import gru
+from keras.src.backend.openvino.rnn import lstm
+from keras.src.backend.openvino.rnn import rnn
diff --git a/keras/src/backend/openvino/core.py b/keras/src/backend/openvino/core.py
new file mode 100644
index 00000000000..8e6bc154694
--- /dev/null
+++ b/keras/src/backend/openvino/core.py
@@ -0,0 +1,265 @@
+import contextlib
+
+import numpy as np
+import openvino as ov
+
+from keras.src import tree
+from keras.src.backend.common import KerasVariable
+from keras.src.backend.common import global_state
+from keras.src.backend.common import standardize_dtype
+from keras.src.backend.common.dtypes import result_type
+from keras.src.backend.common.keras_tensor import KerasTensor
+from keras.src.backend.common.stateless_scope import StatelessScope
+
+SUPPORTS_SPARSE_TENSORS = False
+IS_THREAD_SAFE = True
+
+OPENVINO_DTYPES = {
+    "float16": ov.Type.f16,
+    "float32": ov.Type.f32,
+    "float64": ov.Type.f64,
+    "uint8": ov.Type.u8,
+    "uint16": ov.Type.u16,
+    "uint32": ov.Type.u32,
+    "int8": ov.Type.i8,
+    "int16": ov.Type.i16,
+    "int32": ov.Type.i32,
+    "int64": ov.Type.i64,
+    "bfloat16": ov.Type.bf16,
+    "bool": ov.Type.boolean,
+    "float8_e4m3fn": ov.Type.f8e4m3,
+    "float8_e5m2": ov.Type.f8e5m2,
+}
+
+
+def ov_to_keras_type(ov_type):
+    for _keras_type, _ov_type in OPENVINO_DTYPES.items():
+        if ov_type == _ov_type:
+            return _keras_type
+    raise ValueError(
+        f"Requested OpenVINO type has no keras analogue '{ov_type.to_string()}'"
+    )
+
+
+@contextlib.contextmanager
+def device_scope(device_name):
+    current_device = _parse_device_input(device_name)
+    global_state.set_global_attribute("openvino_device", current_device)
+
+
+def get_device():
+    device = global_state.get_global_attribute("openvino_device", None)
+    if device is None:
+        return "CPU"
+    return device
+
+
+def _parse_device_input(device_name):
+    if isinstance(device_name, str):
+        # We support string value like "cpu:0", "gpu:1", and need to convert
+        # "gpu" to "cuda"
+        device_name = device_name.upper()
+        device_type, _ = device_name.split(":")
+        return device_type
+    else:
+        raise ValueError(
+            "Invalid value for argument `device_name`. "
+            "Expected a string like 'gpu:0' or 'cpu'. "
+            f"Received: device_name='{device_name}'"
+        )
+    return device_name
+
+
+class Variable(KerasVariable):
+    def _initialize(self, value):
+        raise NotImplementedError(
+            "`Variable._initialize` is not supported with openvino backend"
+        )
+
+    def _direct_assign(self, value):
+        raise NotImplementedError(
+            "`Variable._direct_assign` is not supported with openvino backend"
+        )
+
+    def _convert_to_tensor(self, value, dtype=None):
+        raise NotImplementedError(
+            "`Variable._convert_to_tensor` is not supported "
+            "with openvino backend"
+        )
+
+    # Overload native accessor.
+    def __array__(self):
+        raise NotImplementedError(
+            "`Variable.__array__` is not supported with openvino backend"
+        )
+
+
+def convert_to_tensor(x, dtype=None, sparse=None):
+    if sparse:
+        raise ValueError("`sparse=True` is not supported with numpy backend")
+    if dtype is not None:
+        dtype = standardize_dtype(dtype)
+    if isinstance(x, Variable):
+        if dtype and dtype != x.dtype:
+            return x.value.astype(dtype)
+        return x.value
+    if not is_tensor(x) and standardize_dtype(dtype) == "bfloat16":
+        return ov.Tensor(np.asarray(x).astype(dtype))
+    if dtype is None:
+        dtype = result_type(
+            *[getattr(item, "dtype", type(item)) for item in tree.flatten(x)]
+        )
+    return ov.Tensor(np.array(x, dtype=dtype))
+
+
+def convert_to_numpy(x):
+    return np.array(x)
+
+
+def is_tensor(x):
+    if isinstance(x, (ov.runtime.Output)):
+        return False
+    if isinstance(x, ov.Tensor):
+        return True
+    return False
+
+
+def shape(x):
+    return x.shape
+
+
+def cast(x, dtype):
+    raise NotImplementedError("`cast` is not supported with openvino backend")
+
+
+def cond(pred, true_fn, false_fn):
+    raise NotImplementedError("`cond` is not supported with openvino backend")
+
+
+def vectorized_map(function, elements):
+    raise NotImplementedError(
+        "`vectorized_map` is not supported with openvino backend"
+    )
+
+
+# Shape / dtype inference util
+def compute_output_spec(fn, *args, **kwargs):
+    with StatelessScope():
+
+        def has_none_shape(x):
+            if isinstance(x, KerasTensor):
+                return None in x.shape
+            return False
+
+        none_in_shape = any(map(has_none_shape, tree.flatten((args, kwargs))))
+
+        def convert_keras_tensor_to_numpy(x, fill_value=None):
+            if isinstance(x, KerasTensor):
+                shape = list(x.shape)
+                if fill_value:
+                    for i, e in enumerate(shape):
+                        if e is None:
+                            shape[i] = fill_value
+                return np.empty(
+                    shape=shape,
+                    dtype=x.dtype,
+                )
+            return x
+
+        args_1, kwargs_1 = tree.map_structure(
+            lambda x: convert_keras_tensor_to_numpy(x, fill_value=83),
+            (args, kwargs),
+        )
+        outputs_1 = fn(*args_1, **kwargs_1)
+
+        outputs = outputs_1
+
+        if none_in_shape:
+            args_2, kwargs_2 = tree.map_structure(
+                lambda x: convert_keras_tensor_to_numpy(x, fill_value=89),
+                (args, kwargs),
+            )
+            outputs_2 = fn(*args_2, **kwargs_2)
+
+            flat_out_1 = tree.flatten(outputs_1)
+            flat_out_2 = tree.flatten(outputs_2)
+
+            flat_out = []
+            for x1, x2 in zip(flat_out_1, flat_out_2):
+                shape = list(x1.shape)
+                for i, e in enumerate(x2.shape):
+                    if e != shape[i]:
+                        shape[i] = None
+                flat_out.append(KerasTensor(shape, standardize_dtype(x1.dtype)))
+            outputs = tree.pack_sequence_as(outputs_1, flat_out)
+
+        def convert_numpy_to_keras_tensor(x):
+            if is_tensor(x):
+                return KerasTensor(x.shape, standardize_dtype(x.dtype))
+            return x
+
+        output_spec = tree.map_structure(convert_numpy_to_keras_tensor, outputs)
+    return output_spec
+
+
+def scan(f, init, xs=None, length=None, reverse=False, unroll=1):
+    raise NotImplementedError("`scan` is not supported with openvino backend")
+
+
+def scatter(indices, values, shape):
+    raise NotImplementedError(
+        "`scatter` is not supported with openvino backend"
+    )
+
+
+def scatter_update(inputs, indices, updates):
+    raise NotImplementedError(
+        "`scatter_update` is not supported with openvino backend"
+    )
+
+
+def slice(inputs, start_indices, lengths):
+    raise NotImplementedError("`slice` is not supported with openvino backend")
+
+
+def slice_update(inputs, start_indices, updates):
+    raise NotImplementedError(
+        "`slice_update` is not supported with openvino backend"
+    )
+
+
+def while_loop(
+    cond,
+    body,
+    loop_vars,
+    maximum_iterations=None,
+):
+    raise NotImplementedError(
+        "`while_loop` is not supported with openvino backend"
+    )
+
+
+def fori_loop(lower, upper, body_fun, init_val):
+    raise NotImplementedError(
+        "`fori_loop` is not supported with openvino backend"
+    )
+
+
+def stop_gradient(x):
+    return x
+
+
+def unstack(x, num=None, axis=0):
+    raise NotImplementedError(
+        "`unstack` is not supported with openvino backend"
+    )
+
+
+def random_seed_dtype():
+    return "uint32"
+
+
+def custom_gradient(fun):
+    raise NotImplementedError(
+        "`custom_gradient` is not supported with openvino backend"
+    )
diff --git a/keras/src/backend/openvino/image.py b/keras/src/backend/openvino/image.py
new file mode 100644
index 00000000000..aa728845d22
--- /dev/null
+++ b/keras/src/backend/openvino/image.py
@@ -0,0 +1,39 @@
+def rgb_to_grayscale(image, data_format="channels_last"):
+    raise NotImplementedError(
+        "`rgb_to_grayscale` is not supported with openvino backend"
+    )
+
+
+def resize(
+    image,
+    size,
+    interpolation="bilinear",
+    antialias=False,
+    crop_to_aspect_ratio=False,
+    pad_to_aspect_ratio=False,
+    fill_mode="constant",
+    fill_value=0.0,
+    data_format="channels_last",
+):
+    raise NotImplementedError("`resize` is not supported with openvino backend")
+
+
+def affine_transform(
+    image,
+    transform,
+    interpolation="bilinear",
+    fill_mode="constant",
+    fill_value=0,
+    data_format="channels_last",
+):
+    raise NotImplementedError(
+        "`affine_transform` is not supported with openvino backend"
+    )
+
+
+def map_coordinates(
+    input, coordinates, order, fill_mode="constant", fill_value=0.0
+):
+    raise NotImplementedError(
+        "`map_coordinates` is not supported with openvino backend"
+    )
diff --git a/keras/src/backend/openvino/layer.py b/keras/src/backend/openvino/layer.py
new file mode 100644
index 00000000000..334c32958a7
--- /dev/null
+++ b/keras/src/backend/openvino/layer.py
@@ -0,0 +1,2 @@
+class OpenvinoLayer:
+    pass
diff --git a/keras/src/backend/openvino/linalg.py b/keras/src/backend/openvino/linalg.py
new file mode 100644
index 00000000000..3703bd83a0c
--- /dev/null
+++ b/keras/src/backend/openvino/linalg.py
@@ -0,0 +1,52 @@
+def cholesky(a):
+    raise NotImplementedError(
+        "`cholesky` is not supported with openvino backend"
+    )
+
+
+def det(a):
+    raise NotImplementedError("`det` is not supported with openvino backend")
+
+
+def eig(a):
+    raise NotImplementedError("`eig` is not supported with openvino backend")
+
+
+def eigh(a):
+    raise NotImplementedError("`eigh` is not supported with openvino backend")
+
+
+def inv(a):
+    raise NotImplementedError("`inv` is not supported with openvino backend")
+
+
+def lu_factor(a):
+    raise NotImplementedError(
+        "`lu_factor` is not supported with openvino backend"
+    )
+
+
+def norm(x, ord=None, axis=None, keepdims=False):
+    raise NotImplementedError("`norm` is not supported with openvino backend")
+
+
+def qr(x, mode="reduced"):
+    raise NotImplementedError("`qr` is not supported with openvino backend")
+
+
+def solve(a, b):
+    raise NotImplementedError("`solve` is not supported with openvino backend")
+
+
+def solve_triangular(a, b, lower=False):
+    raise NotImplementedError(
+        "`solve_triangular` is not supported with openvino backend"
+    )
+
+
+def svd(x, full_matrices=True, compute_uv=True):
+    raise NotImplementedError("`svd` is not supported with openvino backend")
+
+
+def lstsq(a, b, rcond=None):
+    raise NotImplementedError("`lstsq` is not supported with openvino backend")
diff --git a/keras/src/backend/openvino/math.py b/keras/src/backend/openvino/math.py
new file mode 100644
index 00000000000..d892af8a808
--- /dev/null
+++ b/keras/src/backend/openvino/math.py
@@ -0,0 +1,90 @@
+def segment_sum(data, segment_ids, num_segments=None, sorted=False):
+    raise NotImplementedError(
+        "`segment_sum` is not supported with openvino backend"
+    )
+
+
+def segment_max(data, segment_ids, num_segments=None, sorted=False):
+    raise NotImplementedError(
+        "`segment_max` is not supported with openvino backend"
+    )
+
+
+def top_k(x, k, sorted=False):
+    raise NotImplementedError("`top_k` is not supported with openvino backend")
+
+
+def in_top_k(targets, predictions, k):
+    raise NotImplementedError(
+        "`in_top_k` is not supported with openvino backend"
+    )
+
+
+def logsumexp(x, axis=None, keepdims=False):
+    raise NotImplementedError(
+        "`logsumexp` is not supported with openvino backend"
+    )
+
+
+def qr(x, mode="reduced"):
+    raise NotImplementedError("`qr` is not supported with openvino backend")
+
+
+def extract_sequences(x, sequence_length, sequence_stride):
+    raise NotImplementedError(
+        "`extract_sequences` is not supported with openvino backend"
+    )
+
+
+def fft(x):
+    raise NotImplementedError("`fft` is not supported with openvino backend")
+
+
+def fft2(x):
+    raise NotImplementedError("`fft2` is not supported with openvino backend")
+
+
+def rfft(x, fft_length=None):
+    raise NotImplementedError("`rfft` is not supported with openvino backend")
+
+
+def irfft(x, fft_length=None):
+    raise NotImplementedError("`irfft` is not supported with openvino backend")
+
+
+def stft(
+    x, sequence_length, sequence_stride, fft_length, window="hann", center=True
+):
+    raise NotImplementedError("`stft` is not supported with openvino backend")
+
+
+def istft(
+    x,
+    sequence_length,
+    sequence_stride,
+    fft_length,
+    length=None,
+    window="hann",
+    center=True,
+):
+    raise NotImplementedError("`istft` is not supported with openvino backend")
+
+
+def rsqrt(x):
+    raise NotImplementedError("`rsqrt` is not supported with openvino backend")
+
+
+def erf(x):
+    raise NotImplementedError("`erf` is not supported with openvino backend")
+
+
+def erfinv(x):
+    raise NotImplementedError("`erfinv` is not supported with openvino backend")
+
+
+def solve(a, b):
+    raise NotImplementedError("`solve` is not supported with openvino backend")
+
+
+def norm(x, ord=None, axis=None, keepdims=False):
+    raise NotImplementedError("`norm` is not supported with openvino backend")
diff --git a/keras/src/backend/openvino/nn.py b/keras/src/backend/openvino/nn.py
new file mode 100644
index 00000000000..b141219afbc
--- /dev/null
+++ b/keras/src/backend/openvino/nn.py
@@ -0,0 +1,224 @@
+import numpy as np
+from openvino.runtime import opset14
+
+
+def relu(x):
+    return opset14.relu(x)
+
+
+def relu6(x):
+    raise NotImplementedError("`relu6` is not supported with openvino backend")
+
+
+def sigmoid(x):
+    return opset14.sigmoid(x)
+
+
+def tanh(x):
+    return opset14.tanh(x)
+
+
+def softplus(x):
+    return opset14.softplus(x)
+
+
+def softsign(x):
+    return opset14.softsign(x)
+
+
+def silu(x):
+    return opset14.multiply(x, opset14.sigmoid(x))
+
+
+def log_sigmoid(x):
+    raise NotImplementedError(
+        "`log_sigmoid` is not supported with openvino backend"
+    )
+
+
+def leaky_relu(x, negative_slope=0.2):
+    raise NotImplementedError(
+        "`leaky_relu` is not supported with openvino backend"
+    )
+
+
+def hard_sigmoid(x):
+    alpha = 1 / np.array(6.0, dtype=np.float32)
+    beta = np.array(0.5, dtype=np.float32)
+    return opset14.hard_sigmoid(x, alpha, beta)
+
+
+def hard_silu(x):
+    return opset14.multiply(x, hard_sigmoid(x))
+
+
+def elu(x, alpha=1.0):
+    return opset14.elu(x, alpha)
+
+
+def selu(
+    x,
+    alpha=1.6732632423543772848170429916717,
+    scale=1.0507009873554804934193349852946,
+):
+    return opset14.selu(x, alpha, scale)
+
+
+def gelu(x, approximate=True):
+    approximate_mode = "erf"
+    if approximate:
+        approximate_mode = "tanh"
+    return opset14.gelu(x, approximate_mode)
+
+
+def softmax(x, axis=None):
+    return opset14.softmax(x, axis)
+
+
+def log_softmax(x, axis=None):
+    return opset14.log_softmax(x, axis)
+
+
+def max_pool(
+    inputs,
+    pool_size,
+    strides=None,
+    padding="valid",
+    data_format=None,
+):
+    raise NotImplementedError(
+        "`max_pool` is not supported with openvino backend"
+    )
+
+
+def average_pool(
+    inputs,
+    pool_size,
+    strides,
+    padding,
+    data_format=None,
+):
+    raise NotImplementedError(
+        "`average_pool` is not supported with openvino backend"
+    )
+
+
+def conv(
+    inputs,
+    kernel,
+    strides=1,
+    padding="valid",
+    data_format=None,
+    dilation_rate=1,
+):
+    raise NotImplementedError("`conv` is not supported with openvino backend")
+
+
+def depthwise_conv(
+    inputs,
+    kernel,
+    strides=1,
+    padding="valid",
+    data_format=None,
+    dilation_rate=1,
+):
+    raise NotImplementedError(
+        "`depthwise_conv` is not supported with openvino backend"
+    )
+
+
+def separable_conv(
+    inputs,
+    depthwise_kernel,
+    pointwise_kernel,
+    strides=1,
+    padding="valid",
+    data_format=None,
+    dilation_rate=1,
+):
+    raise NotImplementedError(
+        "`separable_conv` is not supported with openvino backend"
+    )
+
+
+def conv_transpose(
+    inputs,
+    kernel,
+    strides=1,
+    padding="valid",
+    output_padding=None,
+    data_format=None,
+    dilation_rate=1,
+):
+    raise NotImplementedError(
+        "`conv_transpose` is not supported with openvino backend"
+    )
+
+
+def one_hot(x, num_classes, axis=-1, dtype="float32", sparse=False):
+    raise NotImplementedError(
+        "`one_hot` is not supported with openvino backend"
+    )
+
+
+def multi_hot(x, num_classes, axis=-1, dtype="float32", sparse=False):
+    raise NotImplementedError(
+        "`multi_hot` is not supported with openvino backend"
+    )
+
+
+def categorical_crossentropy(target, output, from_logits=False, axis=-1):
+    raise NotImplementedError(
+        "`categorical_crossentropy` is not supported with openvino backend"
+    )
+
+
+def sparse_categorical_crossentropy(target, output, from_logits=False, axis=-1):
+    raise NotImplementedError(
+        "`sparse_categorical_crossentropy` is not supported "
+        "with openvino backend"
+    )
+
+
+def binary_crossentropy(target, output, from_logits=False):
+    raise NotImplementedError(
+        "`binary_crossentropy` is not supported with openvino backend"
+    )
+
+
+def moments(x, axes, keepdims=False, synchronized=False):
+    raise NotImplementedError(
+        "`moments` is not supported with openvino backend"
+    )
+
+
+def batch_normalization(
+    x, mean, variance, axis, offset=None, scale=None, epsilon=1e-3
+):
+    raise NotImplementedError(
+        "`batch_normalization` is not supported with openvino backend"
+    )
+
+
+def ctc_loss(target, output, target_length, output_length, mask_index=0):
+    raise NotImplementedError(
+        "`ctc_loss` is not supported with openvino backend"
+    )
+
+
+def ctc_decode(
+    inputs,
+    sequence_lengths,
+    strategy="greedy",
+    beam_width=100,
+    top_paths=1,
+    merge_repeated=True,
+    mask_index=0,
+):
+    raise NotImplementedError(
+        "`ctc_decode` is not supported with openvino backend"
+    )
+
+
+def psnr(x1, x2, max_val):
+    raise NotImplementedError("`psnr` is not supported with openvino backend")
diff --git a/keras/src/backend/openvino/numpy.py b/keras/src/backend/openvino/numpy.py
new file mode 100644
index 00000000000..47699588115
--- /dev/null
+++ b/keras/src/backend/openvino/numpy.py
@@ -0,0 +1,706 @@
+from openvino.runtime import opset14
+
+from keras.src.backend.common import dtypes
+from keras.src.backend.openvino.core import OPENVINO_DTYPES
+from keras.src.backend.openvino.core import convert_to_tensor
+from keras.src.backend.openvino.core import ov_to_keras_type
+
+
+def _align_operand_types(x1, x2, op_name):
+    x1_type = x1.element_type
+    x2_type = x2.element_type
+    if x1_type.is_dynamic() or x2_type.is_dynamic():
+        raise ValueError(
+            f"'{op_name}' operation is not supported for dynamic operand type "
+            "with openvino backend"
+        )
+    x1_type = ov_to_keras_type(x1_type)
+    x2_type = ov_to_keras_type(x2_type)
+    result_type = dtypes.result_type(x1_type, x2_type)
+    result_type = OPENVINO_DTYPES[result_type]
+    if x1_type != result_type:
+        x1 = opset14.convert(x1, result_type)
+    if x2_type != result_type:
+        x2 = opset14.convert(x2, result_type)
+    return x1, x2
+
+
+def add(x1, x2):
+    x1, x2 = _align_operand_types(x1, x2, "add()")
+    return opset14.add(x1, x2)
+
+
+def einsum(subscripts, *operands, **kwargs):
+    raise NotImplementedError("`einsum` is not supported with openvino backend")
+
+
+def subtract(x1, x2):
+    x1, x2 = _align_operand_types(x1, x2, "subtract()")
+    return opset14.subtract(x1, x2)
+
+
+def matmul(x1, x2):
+    raise NotImplementedError("`matmul` is not supported with openvino backend")
+
+
+def multiply(x1, x2):
+    x1, x2 = _align_operand_types(x1, x2, "multiply()")
+    return opset14.multiply(x1, x2)
+
+
+def mean(x, axis=None, keepdims=False):
+    raise NotImplementedError("`mean` is not supported with openvino backend")
+
+
+def max(x, axis=None, keepdims=False, initial=None):
+    raise NotImplementedError("`max` is not supported with openvino backend")
+
+
+def ones(shape, dtype=None):
+    raise NotImplementedError("`ones` is not supported with openvino backend")
+
+
+def zeros(shape, dtype=None):
+    raise NotImplementedError("`zeros` is not supported with openvino backend")
+
+
+def absolute(x):
+    return opset14.absolute(x)
+
+
+def abs(x):
+    return opset14.absolute(x)
+
+
+def all(x, axis=None, keepdims=False):
+    raise NotImplementedError("`all` is not supported with openvino backend")
+
+
+def any(x, axis=None, keepdims=False):
+    raise NotImplementedError("`any` is not supported with openvino backend")
+
+
+def amax(x, axis=None, keepdims=False):
+    raise NotImplementedError("`amax` is not supported with openvino backend")
+
+
+def amin(x, axis=None, keepdims=False):
+    raise NotImplementedError("`amin` is not supported with openvino backend")
+
+
+def append(x1, x2, axis=None):
+    raise NotImplementedError("`append` is not supported with openvino backend")
+
+
+def arange(start, stop=None, step=None, dtype=None):
+    raise NotImplementedError("`arange` is not supported with openvino backend")
+
+
+def arccos(x):
+    raise NotImplementedError("`arccos` is not supported with openvino backend")
+
+
+def arccosh(x):
+    raise NotImplementedError(
+        "`arccosh` is not supported with openvino backend"
+    )
+
+
+def arcsin(x):
+    raise NotImplementedError("`arcsin` is not supported with openvino backend")
+
+
+def arcsinh(x):
+    raise NotImplementedError(
+        "`arcsinh` is not supported with openvino backend"
+    )
+
+
+def arctan(x):
+    raise NotImplementedError("`arctan` is not supported with openvino backend")
+
+
+def arctan2(x1, x2):
+    raise NotImplementedError(
+        "`arctan2` is not supported with openvino backend"
+    )
+
+
+def arctanh(x):
+    raise NotImplementedError(
+        "`arctanh` is not supported with openvino backend"
+    )
+
+
+def argmax(x, axis=None, keepdims=False):
+    raise NotImplementedError("`argmax` is not supported with openvino backend")
+
+
+def argmin(x, axis=None, keepdims=False):
+    raise NotImplementedError("`argmin` is not supported with openvino backend")
+
+
+def argsort(x, axis=-1):
+    raise NotImplementedError(
+        "`argsort` is not supported with openvino backend"
+    )
+
+
+def array(x, dtype=None):
+    return convert_to_tensor(x, dtype=dtype)
+
+
+def average(x, axis=None, weights=None):
+    raise NotImplementedError(
+        "`average` is not supported with openvino backend"
+    )
+
+
+def bincount(x, weights=None, minlength=0, sparse=False):
+    raise NotImplementedError(
+        "`bincount` is not supported with openvino backend"
+    )
+
+
+def broadcast_to(x, shape):
+    raise NotImplementedError(
+        "`broadcast_to` is not supported with openvino backend"
+    )
+
+
+def ceil(x):
+    raise NotImplementedError("`ceil` is not supported with openvino backend")
+
+
+def clip(x, x_min, x_max):
+    raise NotImplementedError("`clip` is not supported with openvino backend")
+
+
+def concatenate(xs, axis=0):
+    raise NotImplementedError(
+        "`concatenate` is not supported with openvino backend"
+    )
+
+
+def conjugate(x):
+    raise NotImplementedError(
+        "`conjugate` is not supported with openvino backend"
+    )
+
+
+def conj(x):
+    raise NotImplementedError("`conj` is not supported with openvino backend")
+
+
+def copy(x):
+    raise NotImplementedError("`copy` is not supported with openvino backend")
+
+
+def cos(x):
+    raise NotImplementedError("`cos` is not supported with openvino backend")
+
+
+def cosh(x):
+    raise NotImplementedError("`cosh` is not supported with openvino backend")
+
+
+def count_nonzero(x, axis=None):
+    raise NotImplementedError(
+        "`count_nonzero` is not supported with openvino backend"
+    )
+
+
+def cross(x1, x2, axisa=-1, axisb=-1, axisc=-1, axis=None):
+    raise NotImplementedError("`cross` is not supported with openvino backend")
+
+
+def cumprod(x, axis=None, dtype=None):
+    raise NotImplementedError(
+        "`cumprod` is not supported with openvino backend"
+    )
+
+
+def cumsum(x, axis=None, dtype=None):
+    raise NotImplementedError("`cumsum` is not supported with openvino backend")
+
+
+def diag(x, k=0):
+    raise NotImplementedError("`diag` is not supported with openvino backend")
+
+
+def diagonal(x, offset=0, axis1=0, axis2=1):
+    raise NotImplementedError(
+        "`diagonal` is not supported with openvino backend"
+    )
+
+
+def diff(a, n=1, axis=-1):
+    raise NotImplementedError("`diff` is not supported with openvino backend")
+
+
+def digitize(x, bins):
+    raise NotImplementedError(
+        "`digitize` is not supported with openvino backend"
+    )
+
+
+def dot(x, y):
+    raise NotImplementedError("`dot` is not supported with openvino backend")
+
+
+def empty(shape, dtype=None):
+    raise NotImplementedError("`empty` is not supported with openvino backend")
+
+
+def equal(x1, x2):
+    raise NotImplementedError("`equal` is not supported with openvino backend")
+
+
+def exp(x):
+    raise NotImplementedError("`exp` is not supported with openvino backend")
+
+
+def expand_dims(x, axis):
+    raise NotImplementedError(
+        "`expand_dims` is not supported with openvino backend"
+    )
+
+
+def expm1(x):
+    raise NotImplementedError("`expm1` is not supported with openvino backend")
+
+
+def flip(x, axis=None):
+    raise NotImplementedError("`flip` is not supported with openvino backend")
+
+
+def floor(x):
+    raise NotImplementedError("`floor` is not supported with openvino backend")
+
+
+def full(shape, fill_value, dtype=None):
+    raise NotImplementedError("`full` is not supported with openvino backend")
+
+
+def full_like(x, fill_value, dtype=None):
+    raise NotImplementedError(
+        "`full_like` is not supported with openvino backend"
+    )
+
+
+def greater(x1, x2):
+    raise NotImplementedError(
+        "`greater` is not supported with openvino backend"
+    )
+
+
+def greater_equal(x1, x2):
+    raise NotImplementedError(
+        "`greater_equal` is not supported with openvino backend"
+    )
+
+
+def hstack(xs):
+    raise NotImplementedError("`hstack` is not supported with openvino backend")
+
+
+def identity(n, dtype=None):
+    raise NotImplementedError(
+        "`identity` is not supported with openvino backend"
+    )
+
+
+def imag(x):
+    raise NotImplementedError("`imag` is not supported with openvino backend")
+
+
+def isclose(x1, x2):
+    raise NotImplementedError(
+        "`isclose` is not supported with openvino backend"
+    )
+
+
+def isfinite(x):
+    raise NotImplementedError(
+        "`isfinite` is not supported with openvino backend"
+    )
+
+
+def isinf(x):
+    raise NotImplementedError("`isinf` is not supported with openvino backend")
+
+
+def isnan(x):
+    raise NotImplementedError("`isnan` is not supported with openvino backend")
+
+
+def less(x1, x2):
+    raise NotImplementedError("`less` is not supported with openvino backend")
+
+
+def less_equal(x1, x2):
+    raise NotImplementedError(
+        "`less_equal` is not supported with openvino backend"
+    )
+
+
+def linspace(
+    start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0
+):
+    raise NotImplementedError(
+        "`linspace` is not supported with openvino backend"
+    )
+
+
+def log(x):
+    raise NotImplementedError("`log` is not supported with openvino backend")
+
+
+def log10(x):
+    raise NotImplementedError("`log10` is not supported with openvino backend")
+
+
+def log1p(x):
+    raise NotImplementedError("`log1p` is not supported with openvino backend")
+
+
+def log2(x):
+    raise NotImplementedError("`log2` is not supported with openvino backend")
+
+
+def logaddexp(x1, x2):
+    raise NotImplementedError(
+        "`logaddexp` is not supported with openvino backend"
+    )
+
+
+def logical_and(x1, x2):
+    raise NotImplementedError(
+        "`logical_and` is not supported with openvino backend"
+    )
+
+
+def logical_not(x):
+    raise NotImplementedError(
+        "`logical_not` is not supported with openvino backend"
+    )
+
+
+def logical_or(x1, x2):
+    raise NotImplementedError(
+        "`logical_or` is not supported with openvino backend"
+    )
+
+
+def logspace(start, stop, num=50, endpoint=True, base=10, dtype=None, axis=0):
+    raise NotImplementedError(
+        "`logspace` is not supported with openvino backend"
+    )
+
+
+def maximum(x1, x2):
+    raise NotImplementedError(
+        "`maximum` is not supported with openvino backend"
+    )
+
+
+def median(x, axis=None, keepdims=False):
+    raise NotImplementedError("`median` is not supported with openvino backend")
+
+
+def meshgrid(*x, indexing="xy"):
+    raise NotImplementedError(
+        "`meshgrid` is not supported with openvino backend"
+    )
+
+
+def min(x, axis=None, keepdims=False, initial=None):
+    raise NotImplementedError("`min` is not supported with openvino backend")
+
+
+def minimum(x1, x2):
+    raise NotImplementedError(
+        "`minimum` is not supported with openvino backend"
+    )
+
+
+def mod(x1, x2):
+    raise NotImplementedError("`mod` is not supported with openvino backend")
+
+
+def moveaxis(x, source, destination):
+    raise NotImplementedError(
+        "`moveaxis` is not supported with openvino backend"
+    )
+
+
+def nan_to_num(x, nan=0.0, posinf=None, neginf=None):
+    raise NotImplementedError(
+        "`nan_to_num` is not supported with openvino backend"
+    )
+
+
+def ndim(x):
+    raise NotImplementedError("`ndim` is not supported with openvino backend")
+
+
+def nonzero(x):
+    raise NotImplementedError(
+        "`nonzero` is not supported with openvino backend"
+    )
+
+
+def not_equal(x1, x2):
+    raise NotImplementedError(
+        "`not_equal` is not supported with openvino backend"
+    )
+
+
+def zeros_like(x, dtype=None):
+    raise NotImplementedError(
+        "`zeros_like` is not supported with openvino backend"
+    )
+
+
+def ones_like(x, dtype=None):
+    raise NotImplementedError(
+        "`ones_like` is not supported with openvino backend"
+    )
+
+
+def outer(x1, x2):
+    raise NotImplementedError("`outer` is not supported with openvino backend")
+
+
+def pad(x, pad_width, mode="constant", constant_values=None):
+    raise NotImplementedError("`pad` is not supported with openvino backend")
+
+
+def prod(x, axis=None, keepdims=False, dtype=None):
+    raise NotImplementedError("`prod` is not supported with openvino backend")
+
+
+def quantile(x, q, axis=None, method="linear", keepdims=False):
+    raise NotImplementedError(
+        "`quantile` is not supported with openvino backend"
+    )
+
+
+def ravel(x):
+    raise NotImplementedError("`ravel` is not supported with openvino backend")
+
+
+def real(x):
+    raise NotImplementedError("`real` is not supported with openvino backend")
+
+
+def reciprocal(x):
+    raise NotImplementedError(
+        "`reciprocal` is not supported with openvino backend"
+    )
+
+
+def repeat(x, repeats, axis=None):
+    raise NotImplementedError("`repeat` is not supported with openvino backend")
+
+
+def reshape(x, newshape):
+    raise NotImplementedError(
+        "`reshape` is not supported with openvino backend"
+    )
+
+
+def roll(x, shift, axis=None):
+    raise NotImplementedError("`roll` is not supported with openvino backend")
+
+
+def sign(x):
+    raise NotImplementedError("`sign` is not supported with openvino backend")
+
+
+def sin(x):
+    raise NotImplementedError("`sin` is not supported with openvino backend")
+
+
+def sinh(x):
+    raise NotImplementedError("`sinh` is not supported with openvino backend")
+
+
+def size(x):
+    raise NotImplementedError("`size` is not supported with openvino backend")
+
+
+def sort(x, axis=-1):
+    raise NotImplementedError("`sort` is not supported with openvino backend")
+
+
+def split(x, indices_or_sections, axis=0):
+    raise NotImplementedError("`split` is not supported with openvino backend")
+
+
+def stack(x, axis=0):
+    raise NotImplementedError("`stack` is not supported with openvino backend")
+
+
+def std(x, axis=None, keepdims=False):
+    raise NotImplementedError("`std` is not supported with openvino backend")
+
+
+def swapaxes(x, axis1, axis2):
+    raise NotImplementedError(
+        "`swapaxes` is not supported with openvino backend"
+    )
+
+
+def take(x, indices, axis=None):
+    raise NotImplementedError("`take` is not supported with openvino backend")
+
+
+def take_along_axis(x, indices, axis=None):
+    raise NotImplementedError(
+        "`take_along_axis` is not supported with openvino backend"
+    )
+
+
+def tan(x):
+    raise NotImplementedError("`tan` is not supported with openvino backend")
+
+
+def tanh(x):
+    raise NotImplementedError("`tanh` is not supported with openvino backend")
+
+
+def tensordot(x1, x2, axes=2):
+    raise NotImplementedError(
+        "`tensordot` is not supported with openvino backend"
+    )
+
+
+def round(x, decimals=0):
+    raise NotImplementedError("`round` is not supported with openvino backend")
+
+
+def tile(x, repeats):
+    raise NotImplementedError("`tile` is not supported with openvino backend")
+
+
+def trace(x, offset=0, axis1=0, axis2=1):
+    raise NotImplementedError("`trace` is not supported with openvino backend")
+
+
+def tri(N, M=None, k=0, dtype=None):
+    raise NotImplementedError("`tri` is not supported with openvino backend")
+
+
+def tril(x, k=0):
+    raise NotImplementedError("`tril` is not supported with openvino backend")
+
+
+def triu(x, k=0):
+    raise NotImplementedError("`triu` is not supported with openvino backend")
+
+
+def vdot(x1, x2):
+    raise NotImplementedError("`vdot` is not supported with openvino backend")
+
+
+def vstack(xs):
+    raise NotImplementedError("`vstack` is not supported with openvino backend")
+
+
+def vectorize(pyfunc, *, excluded=None, signature=None):
+    raise NotImplementedError(
+        "`vectorize` is not supported with openvino backend"
+    )
+
+
+def where(condition, x1, x2):
+    raise NotImplementedError("`where` is not supported with openvino backend")
+
+
+def divide(x1, x2):
+    x1, x2 = _align_operand_types(x1, x2)
+    return opset14.divide(x1, x2)
+
+
+def divide_no_nan(x1, x2):
+    raise NotImplementedError(
+        "`divide_no_nan` is not supported with openvino backend"
+    )
+
+
+def true_divide(x1, x2):
+    return divide(x1, x2)
+
+
+def power(x1, x2):
+    x1, x2 = _align_operand_types(x1, x2)
+    return opset14.power(x1, x2)
+
+
+def negative(x):
+    return opset14.negative(x)
+
+
+def square(x):
+    raise NotImplementedError("`square` is not supported with openvino backend")
+
+
+def sqrt(x):
+    raise NotImplementedError("`sqrt` is not supported with openvino backend")
+
+
+def squeeze(x, axis=None):
+    raise NotImplementedError(
+        "`squeeze` is not supported with openvino backend"
+    )
+
+
+def transpose(x, axes=None):
+    raise NotImplementedError(
+        "`transpose` is not supported with openvino backend"
+    )
+
+
+def var(x, axis=None, keepdims=False):
+    raise NotImplementedError("`var` is not supported with openvino backend")
+
+
+def sum(x, axis=None, keepdims=False):
+    raise NotImplementedError("`sum` is not supported with openvino backend")
+
+
+def eye(N, M=None, k=0, dtype=None):
+    raise NotImplementedError("`eye` is not supported with openvino backend")
+
+
+def floor_divide(x1, x2):
+    raise NotImplementedError(
+        "`floor_divide` is not supported with openvino backend"
+    )
+
+
+def logical_xor(x1, x2):
+    return opset14.logical_xor(x1, x2)
+
+
+def correlate(x1, x2, mode="valid"):
+    raise NotImplementedError(
+        "`correlate` is not supported with openvino backend"
+    )
+
+
+def select(condlist, choicelist, default=0):
+    raise NotImplementedError("`select` is not supported with openvino backend")
+
+
+def slogdet(x):
+    raise NotImplementedError(
+        "`slogdet` is not supported with openvino backend"
+    )
+
+
+def argpartition(x, kth, axis=-1):
+    raise NotImplementedError(
+        "`argpartition` is not supported with openvino backend"
+    )
diff --git a/keras/src/backend/openvino/random.py b/keras/src/backend/openvino/random.py
new file mode 100644
index 00000000000..4d6565d6f58
--- /dev/null
+++ b/keras/src/backend/openvino/random.py
@@ -0,0 +1,58 @@
+from keras.src.backend.config import floatx
+from keras.src.random.seed_generator import SeedGenerator
+from keras.src.random.seed_generator import draw_seed
+from keras.src.random.seed_generator import make_default_seed
+
+
+def normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
+    raise NotImplementedError("`normal` is not supported with openvino backend")
+
+
+def uniform(shape, minval=0.0, maxval=1.0, dtype=None, seed=None):
+    raise NotImplementedError(
+        "`uniform` is not supported with openvino backend"
+    )
+
+
+def categorical(logits, num_samples, dtype="int64", seed=None):
+    raise NotImplementedError(
+        "`categorical` is not supported with openvino backend"
+    )
+
+
+def randint(shape, minval, maxval, dtype="int32", seed=None):
+    raise NotImplementedError(
+        "`randint` is not supported with openvino backend"
+    )
+
+
+def truncated_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
+    raise NotImplementedError(
+        "`truncated_normal` is not supported with openvino backend"
+    )
+
+
+def dropout(inputs, rate, noise_shape=None, seed=None):
+    raise NotImplementedError(
+        "`dropout` is not supported with openvino backend"
+    )
+
+
+def shuffle(x, axis=0, seed=None):
+    raise NotImplementedError(
+        "`shuffle` is not supported with openvino backend"
+    )
+
+
+def gamma(shape, alpha, dtype=None, seed=None):
+    raise NotImplementedError("`gamma` is not supported with openvino backend")
+
+
+def binomial(shape, counts, probabilities, dtype=None, seed=None):
+    raise NotImplementedError(
+        "`binomial` is not supported with openvino backend"
+    )
+
+
+def beta(shape, alpha, beta, dtype=None, seed=None):
+    raise NotImplementedError("`beta` is not supported with openvino backend")
diff --git a/keras/src/backend/openvino/rnn.py b/keras/src/backend/openvino/rnn.py
new file mode 100644
index 00000000000..70190fc47c8
--- /dev/null
+++ b/keras/src/backend/openvino/rnn.py
@@ -0,0 +1,38 @@
+def rnn(
+    step_function,
+    inputs,
+    initial_states,
+    go_backwards=False,
+    mask=None,
+    constants=None,
+    unroll=False,
+    input_length=None,
+    time_major=False,
+    zero_output_for_mask=False,
+    return_all_outputs=True,
+):
+    raise NotImplementedError("`rnn` is not supported with openvino backend")
+
+
+def lstm(*args, **kwargs):
+    raise NotImplementedError("`lstm` is not supported with openvino backend")
+
+
+def gru(*args, **kwargs):
+    raise NotImplementedError("`gru` is not supported with openvino backend")
+
+
+def unstack(x, axis=0):
+    raise NotImplementedError(
+        "`unstack` is not supported with openvino backend"
+    )
+
+
+def numpy_scan(f, init, xs, reverse=False, mask=None):
+    raise NotImplementedError(
+        "`numpy_scan` is not supported with openvino backend"
+    )
+
+
+def cudnn_ok(*args, **kwargs):
+    return False
diff --git a/keras/src/backend/openvino/trainer.py b/keras/src/backend/openvino/trainer.py
new file mode 100644
index 00000000000..0c1b094392e
--- /dev/null
+++ b/keras/src/backend/openvino/trainer.py
@@ -0,0 +1,245 @@
+import numpy as np
+import openvino as ov
+import openvino.runtime.opset14 as ov_opset
+
+from keras.src import backend
+from keras.src import callbacks as callbacks_module
+from keras.src import tree
+from keras.src.backend.openvino.core import OPENVINO_DTYPES
+from keras.src.backend.openvino.core import get_device
+from keras.src.trainers import trainer as base_trainer
+from keras.src.trainers.data_adapters import data_adapter_utils
+from keras.src.trainers.epoch_iterator import EpochIterator
+from keras.src.utils import traceback_utils
+
+
+class OpenVINOTrainer(base_trainer.Trainer):
+    def __init__(self):
+        super().__init__()
+        self.test_function = None
+        self.predict_function = None
+        self.ov_compiled_model = None
+        self.ov_device = None
+
+    def _unpack_singleton(self, x):
+        if isinstance(x, (list, tuple)) and len(x) == 1:
+            return x[0]
+        return x
+
+    def test_step(self, data):
+        raise NotImplementedError(
+            "`test_step` is not supported with openvino backend"
+        )
+
+    def predict_step(self, data):
+        x, _, _ = data_adapter_utils.unpack_x_y_sample_weight(data)
+        ov_compiled_model = self._get_compiled_model()
+        flatten_x = tree.flatten(x)
+        y_pred = ov_compiled_model(flatten_x)
+        y_pred = self._unpack_singleton(
+            tree.pack_sequence_as(self._outputs_struct, y_pred.to_tuple())
+        )
+        return y_pred
+
+    def make_test_function(self, force=False):
+        if self.test_function is not None and not force:
+            return self.test_function
+
+        def one_test_step(data):
+            data = data[0]
+            return self.test_step(data)
+
+        def multi_test_steps(data):
+            for single_step_data in data:
+                logs = one_test_step([single_step_data])
+            return logs
+
+        if self.steps_per_execution > 1:
+            test_step = multi_test_steps
+        else:
+            test_step = one_test_step
+
+        self.test_function = test_step
+
+    def _get_compiled_model(self):
+        if (
+            self.ov_compiled_model is not None
+            and get_device() == self.ov_device
+        ):
+            return self.ov_compiled_model
+
+        # prepare compiled model from scratch
+        del self.ov_compiled_model
+        ov_inputs = []
+        parameters = []
+        for _input in self._inputs:
+            ov_type = OPENVINO_DTYPES[_input.dtype]
+            ov_shape = _input.shape
+            ov_shape = list(ov_shape)
+            for i in range(len(ov_shape)):
+                if ov_shape[i] is None:
+                    ov_shape[i] = -1
+            param = ov_opset.parameter(shape=ov_shape, dtype=ov_type)
+            parameters.append(param)
+            ov_inputs.append(param.output(0))
+        # build OpenVINO graph ov.Model
+        ov_outputs = self._run_through_graph(
+            ov_inputs, operation_fn=lambda op: op
+        )
+        ov_outputs = tree.flatten(ov_outputs)
+        ov_model = ov.Model(results=ov_outputs, parameters=parameters)
+        self.ov_compiled_model = ov.compile_model(ov_model, get_device())
+        self.ov_device = get_device()
+        return self.ov_compiled_model
+
+    def make_predict_function(self, force=False):
+        if self.predict_function is not None and not force:
+            return self.predict_function
+
+        def one_predict_step(data):
+            data = data[0]
+            return self.predict_step(data)
+
+        def multi_predict_steps(data):
+            outputs = one_predict_step(data[:1])
+
+            for single_step_data in data[1:]:
+                step_outputs = one_predict_step([single_step_data])
+                outputs = tree.map_structure(
+                    lambda t1, t2: np.concatenate([t1, t2]),
+                    outputs,
+                    step_outputs,
+                )
+            return outputs
+
+        if self.steps_per_execution > 1:
+            predict_step = multi_predict_steps
+        else:
+            predict_step = one_predict_step
+
+        self.predict_function = predict_step
+
+    def fit(
+        self,
+        x=None,
+        y=None,
+        batch_size=None,
+        epochs=1,
+        verbose="auto",
+        callbacks=None,
+        validation_split=0.0,
+        validation_data=None,
+        shuffle=True,
+        class_weight=None,
+        sample_weight=None,
+        initial_epoch=0,
+        steps_per_epoch=None,
+        validation_steps=None,
+        validation_batch_size=None,
+        validation_freq=1,
+    ):
+        raise NotImplementedError(
+            "`fit` is not supported with openvino backend"
+        )
+
+    @traceback_utils.filter_traceback
+    def predict(
+        self, x, batch_size=None, verbose="auto", steps=None, callbacks=None
+    ):
+        # Create an iterator that yields batches of input data.
+        epoch_iterator = EpochIterator(
+            x=x,
+            batch_size=batch_size,
+            steps_per_epoch=steps,
+            shuffle=False,
+            steps_per_execution=self.steps_per_execution,
+        )
+
+        # Container that configures and calls callbacks.
+        if not isinstance(callbacks, callbacks_module.CallbackList):
+            callbacks = callbacks_module.CallbackList(
+                callbacks,
+                add_history=True,
+                add_progbar=verbose != 0,
+                verbose=verbose,
+                epochs=1,
+                steps=epoch_iterator.num_batches,
+                model=self,
+            )
+
+        def append_to_outputs(batch_outputs, outputs):
+            if outputs is None:
+                outputs = tree.map_structure(
+                    lambda batch_output: [batch_output],
+                    batch_outputs,
+                )
+            else:
+                tree.map_structure_up_to(
+                    batch_outputs,
+                    lambda output, batch_output: output.append(batch_output),
+                    outputs,
+                    batch_outputs,
+                )
+            return outputs
+
+        self.make_predict_function()
+        self.stop_predicting = False
+        callbacks.on_predict_begin()
+        outputs = None
+        for step, data in epoch_iterator.enumerate_epoch():
+            callbacks.on_predict_batch_begin(step)
+            batch_outputs = self.predict_function(data)
+            outputs = append_to_outputs(batch_outputs, outputs)
+            callbacks.on_predict_batch_end(step, {"outputs": batch_outputs})
+            if self.stop_predicting:
+                break
+        callbacks.on_predict_end()
+        return tree.map_structure_up_to(batch_outputs, np.concatenate, outputs)
+
+    @traceback_utils.filter_traceback
+    def evaluate(
+        self,
+        x=None,
+        y=None,
+        batch_size=None,
+        verbose="auto",
+        sample_weight=None,
+        steps=None,
+        callbacks=None,
+        return_dict=False,
+        **kwargs,
+    ):
+        raise NotImplementedError(
+            "`evaluate` is not supported with openvino backend"
+        )
+
+    def train_on_batch(
+        self,
+        x,
+        y=None,
+        sample_weight=None,
+        class_weight=None,
+        return_dict=False,
+    ):
+        raise NotImplementedError(
+            "`train_on_batch` is not supported with openvino backend"
+        )
+
+    def test_on_batch(
+        self,
+        x,
+        y=None,
+        sample_weight=None,
+        return_dict=False,
+    ):
+        raise NotImplementedError(
+            "`test_on_batch` is not supported with openvino backend"
+        )
+
+    def predict_on_batch(self, x):
+        self.make_predict_function()
+        batch_outputs = self.predict_function([(x,)])
+        batch_outputs = tree.map_structure(
+            backend.convert_to_numpy, batch_outputs
+        )
+        return batch_outputs
diff --git a/keras/src/layers/layer.py b/keras/src/layers/layer.py
index d065fdd2fdf..3756b79e093 100644
--- a/keras/src/layers/layer.py
+++ b/keras/src/layers/layer.py
@@ -52,6 +52,8 @@
     from keras.src.backend.torch.layer import TorchLayer as BackendLayer
 elif backend.backend() == "numpy":
     from keras.src.backend.numpy.layer import NumpyLayer as BackendLayer
+elif backend.backend() == "openvino":
+    from keras.src.backend.openvino.layer import OpenvinoLayer as BackendLayer
 else:
     raise RuntimeError(
         f"Backend '{backend.backend()}' must implement a layer mixin class."
@@ -311,6 +313,9 @@ def __init__(
         self._convert_input_args = True
         # Whether to allow non-tensors as positional arguments in `call()`.
         self._allow_non_tensor_positional_args = False
+        if backend.backend() == "openvino":
+            self._allow_non_tensor_positional_args = True
+
         # Dict of shapes that were used to call `build()`.
         self._build_shapes_dict = None
         # Parent path
diff --git a/keras/src/models/model.py b/keras/src/models/model.py
index 42e5cdddba0..a4788730feb 100644
--- a/keras/src/models/model.py
+++ b/keras/src/models/model.py
@@ -23,6 +23,8 @@
     from keras.src.backend.torch.trainer import TorchTrainer as Trainer
 elif backend.backend() == "numpy":
     from keras.src.backend.numpy.trainer import NumpyTrainer as Trainer
+elif backend.backend() == "openvino":
+    from keras.src.backend.openvino.trainer import OpenVINOTrainer as Trainer
 else:
     raise RuntimeError(
         f"Backend '{backend.backend()}' must implement the Trainer class."
diff --git a/keras/src/utils/backend_utils.py b/keras/src/utils/backend_utils.py
index 3e974aa3e6e..05e637d444b 100644
--- a/keras/src/utils/backend_utils.py
+++ b/keras/src/utils/backend_utils.py
@@ -60,10 +60,10 @@ def __init__(self, backend=None):
         self._backend = backend or backend_module.backend()
 
     def set_backend(self, backend):
-        if backend not in ("tensorflow", "jax", "torch", "numpy"):
+        if backend not in ("tensorflow", "jax", "torch", "numpy", "openvino"):
             raise ValueError(
-                "Available backends are ('tensorflow', 'jax', 'torch' and "
-                f"'numpy'). Received: backend={backend}"
+                "Available backends are ('tensorflow', 'jax', 'torch', "
+                f"'numpy' and 'openvino'). Received: backend={backend}"
             )
         self._backend = backend
 
@@ -92,6 +92,9 @@ def __getattr__(self, name):
                     "Currently, we cannot dynamically import the numpy backend "
                     "because it would disrupt the namespace of the import."
                 )
+        if self._backend == "openvino":
+            module = importlib.import_module("keras.src.backend.openvino")
+            return getattr(module, name)
 
 
 @keras_export("keras.config.set_backend")
diff --git a/requirements.txt b/requirements.txt
index 14ba558acea..3f83fd5e06b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -13,5 +13,8 @@ torchvision>=0.16.0
 jax[cpu]
 flax
 
+# OpenVINO
+openvino~=2024.4.0
+
 # Common deps.
 -r requirements-common.txt