diff --git a/cuda_core/examples/jit_lto_fractal.py b/cuda_core/examples/jit_lto_fractal.py
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+# Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. ALL RIGHTS RESERVED.
+#
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+# ################################################################################
+#
+# This demo aims to illustrate a couple takeaways:
+#
+#   1. How to use the JIT LTO feature provided by the Linker class to link multiple objects together
+#   2. That linking allows for libraries to modify workflows dynamically at runtime
+#
+# This demo mimics a relationship between a library and a user. The user's sole responsibility is to
+# provide device code that generates some art. Whereas the library is responsible for all steps involved in
+# setting up the device, launch configurations and arguments, as well as linking the provided device code.
+#
+# Two algorithms are implemented:
+#   1. A Mandelbrot set
+#   2. A Julia set
+#
+# The user can choose which algorithm to use at runtime and generate the resulting image.
+#
+# ################################################################################
+
+import argparse
+import sys
+
+import cupy as cp
+
+from cuda.core.experimental import Device, LaunchConfig, Linker, LinkerOptions, Program, ProgramOptions, launch
+
+
+# ################################################################################
+#
+# This Mocklibrary is responsible for all steps involved launching the device code.
+#
+# The user is responsible for providing the device code that will be linked into the library's workflow.
+# The provided device code must contain a function with the signature `void generate_art(float* Data)`
+class MockLibrary:
+    def __init__(self):
+        # For this mock library, the main workflow is intentionally kept simple by limiting itself to only calling the
+        # externally defined generate_art function. More involved libraries have the option of applying pre and post
+        # processing steps before calling user-defined device code. Conversely, these responsibilities can be reversed
+        # such that the library owns the bulk of the workflow while allowing users to provide customized pre/post
+        # processing steps.
+        code_main = r"""
+        extern __device__ void generate_art(float* Data);
+
+        extern "C"
+        __global__
+        void main_workflow(float* Data) {
+            // Preprocessing steps can be called here
+            // ...
+
+            // Call the user-defined device code
+            generate_art(Data);
+
+            // Postprocessing steps can be called here
+            // ...
+        }
+        """
+
+        # Most of the launch configurations can be preemptively done before the user provides their device code
+        # Therefore lets compile our main workflow device code now, and link the remaining pieces at a later time
+        self.program_options = ProgramOptions(relocatable_device_code=True)
+        self.main_object_code = Program(code_main, "c++", options=self.program_options).compile("ptx")
+
+        # Setup device state
+        self.dev = Device()
+        self.dev.set_current()
+        self.stream = self.dev.create_stream()
+
+        # Setup a buffer to store the RGBA results for the width and height specified
+        self.width = 1024
+        self.height = 512
+        self.buffer = cp.empty(self.width * self.height * 4, dtype=cp.float32)
+
+        # Setup the launch configuration such that each thread will be generating one pixel, and subdivide
+        # the problem into 16x16 chunks.
+        self.grid = (self.width / 16, self.height / 16, 1.0)
+        self.block = (16, 16, 1)
+        self.config = LaunchConfig(grid=self.grid, block=self.block, stream=self.stream)
+
+    def link(self, user_code, target_type):
+        if target_type == "ltoir":
+            program_options = ProgramOptions(link_time_optimization=True)
+            linker_options = LinkerOptions(link_time_optimization=True)
+        elif target_type == "ptx":
+            program_options = self.program_options
+            linker_options = LinkerOptions()
+        else:
+            raise AssertionError(f"Invalid {target_type=}")
+
+        # First, user-defined code is compiled into a PTX object code
+        user_object_code = Program(user_code, "c++", options=program_options).compile(target_type)
+
+        # Then a Linker is created to link the main object code with the user-defined code
+        linker = Linker(self.main_object_code, user_object_code, options=linker_options)
+
+        # We emit the linked code as cubin
+        linked_code = linker.link("cubin")
+
+        # Now we're ready to retrieve the main device function and execute our library's workflow
+        return linked_code.get_kernel("main_workflow")
+
+    def run(self, kernel):
+        launch(kernel, self.config, self.buffer.data.ptr)
+        self.stream.sync()
+
+        # Return the result as a NumPy array (on host).
+        return cp.asnumpy(self.buffer).reshape(self.height, self.width, 4)
+
+
+# Now lets proceed with code from the user's perspective!
+#
+# ################################################################################
+
+# Simple implementation of Mandelbrot set from Wikipedia
+# http://en.wikipedia.org/wiki/Mandelbrot_set
+#
+# Note that this kernel is meant to be a simple, straight-forward
+# implementation. No attempt is made to optimize this GPU code.
+code_mandelbrot = r"""
+__device__
+void generate_art(float* Data) {
+    // Which pixel am I?
+    unsigned DataX = blockIdx.x * blockDim.x + threadIdx.x;
+    unsigned DataY = blockIdx.y * blockDim.y + threadIdx.y;
+    unsigned Width = gridDim.x * blockDim.x;
+    unsigned Height = gridDim.y * blockDim.y;
+
+    float R, G, B, A;
+
+    // Scale coordinates to (-2.5, 1) and (-1, 1)
+
+    float NormX = (float)DataX / (float)Width;
+    NormX *= 3.5f;
+    NormX -= 2.5f;
+
+    float NormY = (float)DataY / (float)Height;
+    NormY *= 2.0f;
+    NormY -= 1.0f;
+
+    float X0 = NormX;
+    float Y0 = NormY;
+
+    float X = 0.0f;
+    float Y = 0.0f;
+
+    unsigned Iter = 0;
+    unsigned MaxIter = 1000;
+
+    // Iterate
+    while(X*X + Y*Y < 4.0f && Iter < MaxIter) {
+        float XTemp = X*X - Y*Y + X0;
+        Y = 2.0f*X*Y + Y0;
+
+        X = XTemp;
+
+        Iter++;
+    }
+
+    unsigned ColorG = Iter % 50;
+    unsigned ColorB = Iter % 25;
+
+    R = 0.0f;
+    G = (float)ColorG / 50.0f;
+    B = (float)ColorB / 25.0f;
+    A = 1.0f;
+
+    unsigned i = DataY*Width*4+DataX*4;
+    Data[i+0] = R;
+    Data[i+1] = G;
+    Data[i+2] = B;
+    Data[i+3] = A;
+}
+"""
+
+# Simple implementation of Julia set from Wikipedia
+# http://en.wikipedia.org/wiki/Julia_set
+#
+# Note that this kernel is meant to be a simple, straight-forward
+# implementation. No attempt is made to optimize this GPU code.
+code_julia = r"""
+__device__
+void generate_art(float* Data) {
+    // Which pixel am I?
+    unsigned DataX = blockIdx.x * blockDim.x + threadIdx.x;
+    unsigned DataY = blockIdx.y * blockDim.y + threadIdx.y;
+    unsigned Width = gridDim.x * blockDim.x;
+    unsigned Height = gridDim.y * blockDim.y;
+
+    float R, G, B, A;
+
+    // Scale coordinates to (-2, 2) for both x and y
+    // Scale coordinates to (-2.5, 1) and (-1, 1)
+    float X = (float)DataX / (float)Width;
+    X *= 4.0f;
+    X -= 2.0f;
+
+    float Y = (float)DataY / (float)Height;
+    Y *= 2.0f;
+    Y -= 1.0f;
+
+    // Julia set uses a fixed constant C
+    float Cx = -0.8f;  // Try different values for different patterns
+    float Cy = 0.156f;   // Try different values for different patterns
+
+    unsigned Iter = 0;
+    unsigned MaxIter = 1000;
+
+    // Iterate
+    while(X*X + Y*Y < 4.0f && Iter < MaxIter) {
+        float XTemp = X*X - Y*Y + Cx;
+        Y = 2.0f*X*Y + Cy;
+        X = XTemp;
+        Iter++;
+    }
+
+    unsigned ColorG = Iter % 50;
+    unsigned ColorB = Iter % 25;
+
+    R = 0.0f;
+    G = (float)ColorG / 50.0f;
+    B = (float)ColorB / 25.0f;
+    A = 1.0f;
+
+    unsigned i = DataY*Width*4+DataX*4;
+    Data[i+0] = R;
+    Data[i+1] = G;
+    Data[i+2] = B;
+    Data[i+3] = A;
+}
+"""
+
+
+def main():
+    # Parse command line arguments
+    # Two different kernels are implemented with unique algorithms, and the user can choose which one should be used
+    # Both kernels fulfill the signature required by the MockLibrary: `void generate_art(float* Data)`
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--target",
+        "-t",
+        type=str,
+        default="all",
+        choices=["mandelbrot", "julia", "all"],
+        help="Type of visualization to generate",
+    )
+    parser.add_argument(
+        "--format",
+        "-f",
+        type=str,
+        default="ltoir",
+        choices=["ptx", "ltoir"],
+        help="Type of intermediate format for the device functions to be linked",
+    )
+    parser.add_argument(
+        "--display",
+        "-d",
+        action="store_true",
+        help="Display the generated images",
+    )
+    args = parser.parse_args()
+
+    if args.display:
+        try:
+            import matplotlib.pyplot as plt
+        except ImportError:
+            print("this example requires matplotlib installed in order to display the image", file=sys.stderr)
+            sys.exit(0)
+
+    result_to_display = []
+    lib = MockLibrary()
+
+    # Process mandelbrot option
+    if args.target in ("mandelbrot", "all"):
+        # The library will compile and link their main kernel with the provided Mandelbrot kernel
+        kernel = lib.link(code_mandelbrot, args.format)
+        result = lib.run(kernel)
+        result_to_display.append((result, "Mandelbrot"))
+
+    # Process julia option
+    if args.target in ("julia", "all"):
+        # Likewise, the same library can be configured to instead use the provided Julia kernel
+        kernel = lib.link(code_julia, args.format)
+        result = lib.run(kernel)
+        result_to_display.append((result, "Julia"))
+
+    # Display the generated images if requested
+    if args.display:
+        fig = plt.figure()
+        for i, (image, title) in enumerate(result_to_display):
+            axs = fig.add_subplot(len(result_to_display), 1, i + 1)
+            axs.imshow(image)
+            axs.set_title(title)
+            axs.axis("off")
+        plt.show()
+
+
+if __name__ == "__main__":
+    main()
+    print("done!")