diff --git a/tools/README.md b/tools/README.md
index 44839cbaa9f..3306dd994e2 100644
--- a/tools/README.md
+++ b/tools/README.md
@@ -153,3 +153,17 @@ The input file should contain the following columns:
 ```shell
 python visualize_compression_results.py --input-file data/llama2_asym.csv --output-dir output_dir
 ```
+
+## Profiler
+
+The Profiler is a tool for collecting and analyzing activation statistics from OpenVINO models. It enables layer-by-layer profiling of model activations using NNCF infrastructure, making it useful for debugging quantization and compression issues, comparing model variants, and understanding activation distributions.
+
+Key features:
+
+- Collect raw activations at input and output of specific layers using regex pattern matching
+- Calculate custom statistics (min, max, mean, std, percentiles, etc.) on collected activations
+- Compare activations between two model variants (e.g., FP32 vs INT8) with built-in and custom metrics
+- Visualize activation distributions and comparison results with matplotlib
+- Extensible architecture allowing registration of custom statistics, comparators, and visualizers
+
+See [tiny_llama_profiling.ipynb](profiler/tiny_llama_profiling.ipynb) for a complete usage example demonstrating how to profile an OpenVino model, collect activation statistics, and compare FP32 vs INT8 quantized variants.
diff --git a/tools/profiler/profiler.py b/tools/profiler/profiler.py
new file mode 100644
index 00000000000..7d1847b820b
--- /dev/null
+++ b/tools/profiler/profiler.py
@@ -0,0 +1,815 @@
+# Copyright (c) 2025 Intel Corporation
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#      http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+NNCF Profiler Tool
+
+A tool for collecting activation statistics from OpenVINO models using NNCF infrastructure.
+This profiler can collect raw activations at specific layers matching regex patterns.
+"""
+
+import re
+from typing import Any, Optional, Pattern, Union
+
+import numpy as np
+import openvino.runtime as ov
+import pandas as pd
+
+from nncf.common.tensor_statistics.statistic_point import StatisticPoint
+from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer
+from nncf.openvino.graph.nncf_graph_builder import GraphConverter
+from nncf.openvino.graph.transformations.commands import OVTargetPoint
+from nncf.openvino.graph.transformations.commands import TargetType
+from nncf.openvino.statistics.aggregator import OVStatisticsAggregator
+from nncf.openvino.statistics.collectors import get_raw_stat_collector
+
+# Type aliases for better readability
+ActivationData = dict[str, dict[str, list[np.ndarray]]]
+StatisticPointsList = list[Any]
+
+
+class NNCFProfiler:
+    """
+    A profiler for collecting activation statistics from OpenVINO models.
+
+    This class provides functionality to:
+    - Collect raw activations at input and output of specific layers
+    - Filter layers using regex patterns
+    - Aggregate statistics across multiple samples
+    - Calculate activation statistics
+    - Compare activations between two model variants
+    - Register custom comparator metrics and visualizers
+
+    Attributes:
+        dataset: Dataset object for collecting statistics (typically nncf.Dataset)
+        pattern: Regex pattern to match layer names for activation collection
+        num_samples: Number of samples to collect from the dataset
+
+    Example usage:
+        ```python
+        import openvino.runtime as ov
+        from nncf import Dataset
+
+        model = ov.Core().read_model("model.xml")
+        dataset = Dataset(data_source, transform_fn)
+
+        profiler = NNCFProfiler(
+            pattern=r'__module.model.layers.\d+.self_attn',
+            dataset=dataset,
+            num_samples=100
+        )
+
+        # Collect activations from specific layers
+        activations = profiler.collect_activations(model)
+
+        # Access collected data
+        for layer_name, data in activations.items():
+            input_activations = data['in']   # List[np.ndarray]
+            output_activations = data['out']  # List[np.ndarray]
+
+        # Register custom metrics
+        @NNCFProfiler.comparator("custom_metric")
+        def custom_metric(a, b):
+            return float(np.abs(a - b).max())
+
+        # Compare with custom metrics
+        comparison = profiler.compare_activations(
+            acts1, acts2,
+            metrics=["mean_diff", "custom_metric"]
+        )
+        ```
+    """
+
+    # Class-level registries for comparators, visualizers, and statistics
+    COMPARATORS: dict[str, Any] = {}
+    VISUALIZERS: dict[str, Any] = {}
+    STATISTICS: dict[str, Any] = {}
+
+    @classmethod
+    def comparator(cls, name: str):
+        """
+        Decorator to register a custom comparator function for activation comparison.
+
+        A comparator function should take two arguments (data1, data2) representing
+        activation arrays and return a scalar metric value.
+
+        Example:
+            @NNCFProfiler.comparator("mean_diff")
+            def mean_diff(a: np.ndarray, b: np.ndarray) -> float:
+                return float((a - b).mean())
+
+        Args:
+            name: Name to register the comparator under
+
+        Returns:
+            Decorator function that registers the comparator
+        """
+
+        def decorator(func):
+            cls.COMPARATORS[name] = func
+            return func
+
+        return decorator
+
+    @classmethod
+    def visualizer(cls, name: str):
+        """
+        Decorator to register a custom visualizer function for plotting activations.
+
+        A visualizer function should accept activation data and plotting parameters,
+        and return a matplotlib figure or display a plot.
+
+        Example:
+            @NNCFProfiler.visualizer("hist")
+            def hist_plot(data: ActivationData, layer: str, **kwargs):
+                import matplotlib.pyplot as plt
+                # ... plotting code ...
+                return plt.gcf()
+
+        Args:
+            name: Name to register the visualizer under
+
+        Returns:
+            Decorator function that registers the visualizer
+        """
+
+        def decorator(func):
+            cls.VISUALIZERS[name] = func
+            return func
+
+        return decorator
+
+    @classmethod
+    def statistic(cls, name: str):
+        """
+        Decorator to register a custom statistic function for calculating activation statistics.
+
+        A statistic function should take a numpy array representing flattened activation values
+        and return a scalar statistic value.
+
+        Example:
+            @NNCFProfiler.statistic("median")
+            def median(vals: np.ndarray) -> float:
+                return float(np.median(vals))
+
+        Args:
+            name: Name to register the statistic under
+
+        Returns:
+            Decorator function that registers the statistic
+        """
+
+        def decorator(func):
+            cls.STATISTICS[name] = func
+            return func
+
+        return decorator
+
+    def __init__(self, pattern: Union[str, Pattern[str]], dataset: Any, num_samples: int) -> None:
+        """
+        Initialize the NNCF Profiler.
+
+        Args:
+            pattern: Regex pattern (string or compiled Pattern object) to match layer names.
+                     Examples: r'self_attn', r'__module.model.layers.\d+.mlp'
+            dataset: Dataset object for collecting statistics. Should be compatible with
+                     NNCF's OVStatisticsAggregator (typically nncf.Dataset)
+            num_samples: Number of samples to collect from the dataset for profiling.
+                        Larger values provide more accurate statistics but consume more memory
+        """
+        self.dataset: Any = dataset
+        self.pattern: Union[str, Pattern[str]] = pattern
+        self.num_samples: int = num_samples
+
+    def _get_statistic_points(
+        self, model: ov.Model, graph: Any, nodes: list[Any], subset_size: int
+    ) -> StatisticPointsContainer:
+        """
+        Create statistic points for collecting activations at input and output of specified nodes.
+
+        This is an internal helper method that configures collection points for both pre-layer
+        (input) and post-layer (output) activations for each target node.
+
+        Args:
+            model: OpenVINO model being profiled
+            graph: NNCF graph representation of the model
+            nodes: List of NNCF graph nodes to collect statistics from
+            subset_size: Number of samples to collect for each statistic point
+
+        Returns:
+            StatisticPointsContainer with configured collection points for all target nodes
+        """
+        statistic_container = StatisticPointsContainer()
+        OUTPUT_PORT_OF_NODE = 0
+        INPUT_PORT_OF_NODE = 0
+
+        # Collection of statistics after/before layers.
+        for node in nodes:
+            node_name = node.node_name
+            channel_axis = node.metatype.output_channel_axis
+            if channel_axis is None:
+                channel_axis = -1
+
+            # For layers with weights, there is only one output port - 0.
+            statistic_point_out = OVTargetPoint(TargetType.POST_LAYER_OPERATION, node_name, port_id=OUTPUT_PORT_OF_NODE)
+            stat_collector_out = get_raw_stat_collector(num_samples=subset_size)
+            statistic_container.add_statistic_point(
+                StatisticPoint(
+                    target_point=statistic_point_out, tensor_collector=stat_collector_out, algorithm="collect"
+                )
+            )
+
+            # For layers with weights, there is only one output port - 0.
+            statistic_point_in = OVTargetPoint(TargetType.PRE_LAYER_OPERATION, node_name, port_id=INPUT_PORT_OF_NODE)
+            stat_collector_in = get_raw_stat_collector(num_samples=subset_size)
+            statistic_container.add_statistic_point(
+                StatisticPoint(target_point=statistic_point_in, tensor_collector=stat_collector_in, algorithm="collect")
+            )
+
+        return statistic_container
+
+    def collect_activations(
+        self,
+        model: ov.Model,
+        dataset: Optional[Any] = None,
+        pattern: Optional[Union[str, Pattern[str]]] = None,
+        num_samples: Optional[int] = None,
+    ) -> ActivationData:
+        """
+        Collect activation statistics from layers matching the specified pattern.
+
+        This method profiles the model by running inference on the dataset and collecting
+        raw activation values at both the input and output of matching layers.
+
+        Args:
+            model: OpenVINO model to profile
+            dataset: Optional dataset to use for collection. If None, uses the instance's dataset.
+                    Should be compatible with NNCF's OVStatisticsAggregator
+            pattern: Optional regex pattern to match layer names. If None, uses the instance's pattern.
+                    Examples: r'self_attn', r'__module.model.layers.\d+.mlp'
+            num_samples: Optional number of samples to collect. If None, uses the instance's num_samples
+
+        Returns:
+            Dictionary mapping layer names to their collected activations:
+            {
+                'layer_name_1': {
+                    'in': [array1, array2, ...],   # List of input activation arrays
+                    'out': [array1, array2, ...]   # List of output activation arrays
+                },
+                'layer_name_2': {...},
+                ...
+            }
+            Each array is a numpy array containing activation values for one sample.
+
+        Raises:
+            ValueError: If no layers match the specified pattern
+        """
+        # Use provided parameters or fall back to instance defaults
+        dataset = dataset if dataset is not None else self.dataset
+        pattern = pattern if pattern is not None else self.pattern
+        num_samples = num_samples if num_samples is not None else self.num_samples
+
+        # Compile pattern if it's a string
+        regexp: Pattern[str] = re.compile(pattern) if isinstance(pattern, str) else pattern
+
+        # Build graph and create statistics aggregator
+        graph = GraphConverter.create_nncf_graph(model)
+        statistics_aggregator = OVStatisticsAggregator(dataset)
+
+        # Find target nodes matching the pattern
+        node_keys = graph.get_all_node_keys()
+        target_names = [key for key in node_keys if regexp.search(key)]
+
+        if not target_names:
+            msg = f"No layers found matching pattern: {pattern}"
+            raise ValueError(msg)
+
+        target_ops = [graph.get_node_by_key(name) for name in target_names]
+
+        # Register statistic collection points and collect statistics
+        statistic_points = self._get_statistic_points(model, graph, target_ops, num_samples)
+        statistics_aggregator.register_statistic_points(statistic_points)
+        statistics_aggregator.collect_statistics(model, graph)
+
+        # Extract and convert collected statistics to numpy arrays
+        result: ActivationData = {}
+        for layer_name, statistic_points_list in statistics_aggregator.statistic_points.items():
+            # Extract input activations (index 1 in statistic_points_list)
+            in_container = list(
+                statistic_points_list[1].algorithm_to_tensor_collectors["collect"][0].aggregators.values()
+            )[0]._container
+            in_vals = [np.array(elem.data) for elem in in_container]
+
+            # Extract output activations (index 0 in statistic_points_list)
+            out_container = list(
+                statistic_points_list[0].algorithm_to_tensor_collectors["collect"][0].aggregators.values()
+            )[0]._container
+            out_vals = [np.array(elem.data) for elem in out_container]
+
+            result[layer_name] = {"in": in_vals, "out": out_vals}
+
+        return result
+
+    def calculate_stats(self, data: ActivationData, statistics: Optional[list[str]] = None) -> pd.DataFrame:
+        """
+        Calculate custom statistics for collected activations using registered statistic functions.
+
+        Args:
+            data: Activation data dictionary as returned by collect_activations().
+                  Format: {layer_name: {'in': [arrays...], 'out': [arrays...]}}
+            statistics: List of statistic names to compute. If None, uses all registered statistics.
+                       Statistics must be registered in STATISTICS registry.
+
+        Returns:
+            pandas DataFrame with columns:
+            - name: Layer name
+            - type: Activation type ('in' or 'out')
+            - <statistic_name>: One column for each requested statistic
+
+        Raises:
+            ValueError: If any statistic is not found in STATISTICS registry
+
+        Example:
+            >>> # Register custom statistic
+            >>> @NNCFProfiler.statistic("median")
+            >>> def median(vals):
+            ...     return float(np.median(vals))
+            >>>
+            >>> profiler.calculate_stats(acts, statistics=["min", "max", "median"])
+        """
+        # Use all registered statistics if not specified
+        if statistics is None:
+            statistics = list(self.STATISTICS.keys())
+
+        # Validate all statistics are registered
+        for stat in statistics:
+            if stat not in self.STATISTICS:
+                msg = f"Unknown statistic '{stat}'. Available: {list(self.STATISTICS.keys())}"
+                raise ValueError(msg)
+
+        activation_types = ["in", "out"]
+        result_data = {"name": [], "type": []}
+
+        # Initialize columns for each statistic
+        for stat in statistics:
+            result_data[stat] = []
+
+        # Calculate statistics for each layer and activation type
+        for layer_name in data:
+            result_data["name"].extend([layer_name] * len(activation_types))
+            result_data["type"].extend(activation_types)
+
+            for activation_type in activation_types:
+                # Get all activation arrays for this type and flatten them
+                activation_arrays = data[layer_name][activation_type]
+                flattened_vals = np.concatenate([arr.flatten() for arr in activation_arrays])
+
+                # Calculate each statistic using registered functions
+                for stat in statistics:
+                    stat_value = self.STATISTICS[stat](flattened_vals)
+                    result_data[stat].append(stat_value)
+
+        return pd.DataFrame(result_data)
+
+    def compare_activations(
+        self,
+        data1: ActivationData,
+        data2: ActivationData,
+        metrics: Optional[list[str]] = None,
+        statistics: Optional[list[str]] = None,
+    ) -> pd.DataFrame:
+        """
+        Compare activations between two model variants using specified metrics and statistics.
+
+        Args:
+            data1: Activation data from the first model (baseline)
+            data2: Activation data from the second model (modified)
+            metrics: List of comparator metric names to compute. If None, uses all registered comparators.
+                    Metrics must be registered in COMPARATORS registry.
+            statistics: List of statistic names to compute for each dataset. If None, uses all registered statistics.
+                       Statistics must be registered in STATISTICS registry.
+
+        Returns:
+            pandas DataFrame containing:
+            - name: Layer name
+            - type: Activation type ('in' or 'out')
+            - <stat>_data1: Statistics for data1 (e.g., 'mean_data1', 'std_data1')
+            - <stat>_data2: Statistics for data2 (e.g., 'mean_data2', 'std_data2')
+            - <metric>: Comparator metrics (e.g., 'mean_diff', 'relative_diff')
+
+        Raises:
+            ValueError: If any metric or statistic is not found in respective registries
+
+        Example:
+            >>> profiler.compare_activations(acts_fp32, acts_int8,
+            ...                  metrics=["mean_diff", "relative_diff"],
+            ...                  statistics=["min", "max", "mean", "std"])
+        """
+        # Verify both datasets have the same layers
+        assert set(data1.keys()) == set(data2.keys()), "Activation datasets must contain the same layer names"
+
+        # Use all registered comparators if metrics not specified
+        if metrics is None:
+            metrics = list(self.COMPARATORS.keys())
+
+        # Use all registered statistics if not specified
+        if statistics is None:
+            statistics = list(self.STATISTICS.keys())
+
+        # Validate all metrics are registered
+        for metric in metrics:
+            if metric not in self.COMPARATORS:
+                msg = f"Unknown comparator '{metric}'. Available: {list(self.COMPARATORS.keys())}"
+                raise ValueError(msg)
+
+        # Validate all statistics are registered
+        for stat in statistics:
+            if stat not in self.STATISTICS:
+                msg = f"Unknown statistic '{stat}'. Available: {list(self.STATISTICS.keys())}"
+                raise ValueError(msg)
+
+        # Calculate statistics for data1 and data2 using calculate_stats() method
+        stats1_df = self.calculate_stats(data1, statistics=statistics)
+        stats2_df = self.calculate_stats(data2, statistics=statistics)
+
+        # Rename statistic columns to indicate which dataset they belong to
+        for stat in statistics:
+            stats1_df.rename(columns={stat: f"{stat}_data1"}, inplace=True)
+            stats2_df.rename(columns={stat: f"{stat}_data2"}, inplace=True)
+
+        # Merge statistics dataframes
+        result = stats1_df.copy()
+        for col in stats2_df.columns:
+            if col not in ["name", "type"]:
+                result[col] = stats2_df[col]
+
+        # Calculate comparator metrics for each layer and activation type
+        activation_types = ["in", "out"]
+
+        # Initialize columns for each metric
+        for metric in metrics:
+            result[metric] = None
+
+        # Calculate metrics row by row
+        row_idx = 0
+        for layer_name in data1:
+            for activation_type in activation_types:
+                vals1_list = data1[layer_name][activation_type]
+                vals2_list = data2[layer_name][activation_type]
+
+                # Verify dimensions match
+                assert len(vals1_list) == len(vals2_list), (
+                    f"Number of samples mismatch for {layer_name} {activation_type}"
+                )
+
+                for i in range(len(vals1_list)):
+                    assert vals1_list[i].shape == vals2_list[i].shape, (
+                        f"Shape mismatch for {layer_name} {activation_type} sample {i}"
+                    )
+
+                # Flatten and concatenate all samples
+                vals1 = np.concatenate([arr.flatten() for arr in vals1_list])
+                vals2 = np.concatenate([arr.flatten() for arr in vals2_list])
+
+                # Calculate each metric using registered comparators
+                for metric in metrics:
+                    metric_value = self.COMPARATORS[metric](vals1, vals2)
+                    result.at[row_idx, metric] = metric_value
+
+                row_idx += 1
+
+        return result
+
+    def plot(self, plot_type: str, *args, **kwargs):
+        """
+        Generate visualizations using registered visualizer functions.
+
+        Args:
+            plot_type: Name of the visualizer to use (must be registered in VISUALIZERS)
+            *args: Positional arguments to pass to the visualizer
+            **kwargs: Keyword arguments to pass to the visualizer
+
+        Returns:
+            Return value from the visualizer function (typically a matplotlib figure)
+
+        Raises:
+            ValueError: If plot_type is not found in VISUALIZERS registry
+
+        Example:
+            >>> profiler.plot("hist", acts_fp, acts_int, layer="layer_7")
+            >>> profiler.plot("mean_std", acts_fp)
+            >>> profiler.plot("metric", cmp, metric="relative_diff")
+        """
+        if plot_type not in self.VISUALIZERS:
+            msg = f"Unknown plot type '{plot_type}'. Available: {list(self.VISUALIZERS.keys())}"
+            raise ValueError(msg)
+        return self.VISUALIZERS[plot_type](*args, **kwargs)
+
+
+# Built-in comparator functions
+@NNCFProfiler.comparator("mean_diff")
+def _mean_diff(a: np.ndarray, b: np.ndarray) -> float:
+    """Calculate the mean of the difference between two activation arrays."""
+    return float((a - b).mean())
+
+
+@NNCFProfiler.comparator("std_diff")
+def _std_diff(a: np.ndarray, b: np.ndarray) -> float:
+    """Calculate the standard deviation of the difference between two activation arrays."""
+    return float((a - b).std())
+
+
+@NNCFProfiler.comparator("relative_diff")
+def _relative_diff(a: np.ndarray, b: np.ndarray) -> float:
+    """Calculate the mean relative difference between two activation arrays."""
+    epsilon = 1e-8
+    return float((np.abs(a - b) / (np.abs(a) + epsilon)).mean())
+
+
+# Built-in statistic functions
+@NNCFProfiler.statistic("min")
+def _stat_min(vals: np.ndarray) -> float:
+    """Calculate the minimum value of activations."""
+    return float(vals.min())
+
+
+@NNCFProfiler.statistic("max")
+def _stat_max(vals: np.ndarray) -> float:
+    """Calculate the maximum value of activations."""
+    return float(vals.max())
+
+
+@NNCFProfiler.statistic("mean")
+def _stat_mean(vals: np.ndarray) -> float:
+    """Calculate the mean value of activations."""
+    return float(vals.mean())
+
+
+@NNCFProfiler.statistic("std")
+def _stat_std(vals: np.ndarray) -> float:
+    """Calculate the standard deviation of activations."""
+    return float(vals.std())
+
+
+@NNCFProfiler.statistic("median")
+def _stat_median(vals: np.ndarray) -> float:
+    """Calculate the median value of activations."""
+    return float(np.median(vals))
+
+
+@NNCFProfiler.statistic("percentile_95")
+def _stat_percentile_95(vals: np.ndarray) -> float:
+    """Calculate the 95th percentile of activations."""
+    return float(np.percentile(vals, 95))
+
+
+@NNCFProfiler.statistic("percentile_99")
+def _stat_percentile_99(vals: np.ndarray) -> float:
+    """Calculate the 99th percentile of activations."""
+    return float(np.percentile(vals, 99))
+
+
+@NNCFProfiler.statistic("abs_mean")
+def _stat_abs_mean(vals: np.ndarray) -> float:
+    """Calculate the mean of absolute values of activations."""
+    return float(np.abs(vals).mean())
+
+
+@NNCFProfiler.visualizer("compare_detailed")
+def _compare_detailed_plot(
+    data1: ActivationData,
+    data2: ActivationData,
+    activation_type: Optional[str] = None,
+    bins: int = 100,
+    show_histograms: bool = True,
+    show_summary: bool = True,
+    display_figures: bool = False,
+    data1_label: str = "data1",
+    data2_label: str = "data2",
+    **kwargs,
+):
+    """
+    Create detailed comparison plots between two activation datasets.
+
+    This visualizer creates:
+    1. Histogram overlays for each layer (if show_histograms=True)
+    2. A summary plot with 4 subplots showing:
+       - Mean values for data1 across layers
+       - Mean values for data2 across layers
+       - Mean difference (data1 - data2) across layers
+       - Relative difference across layers
+
+    Args:
+        data1: First activation dataset (e.g., FP32 model)
+        data2: Second activation dataset (e.g., quantized model)
+        activation_type: 'in', 'out', or None. If None, processes both 'in' and 'out' activations
+        bins: Number of bins for histograms
+        show_histograms: Whether to show individual histogram plots for each layer
+        show_summary: Whether to show the summary plot with statistics
+        display_figures: Whether to display figures immediately. If False, figures are only returned.
+                        Set to False to avoid memory issues with many figures.
+        data1_label: Label for data1 in plots (e.g., 'fp16', 'fp32')
+        data2_label: Label for data2 in plots (e.g., 'int8', 'fp8')
+        **kwargs: Additional arguments for plotting
+
+    Returns:
+        List of matplotlib figures (histograms + summary plot)
+
+    Example:
+        >>> # Compare both input and output activations
+        >>> figs = profiler.plot("compare_detailed", acts_fp32, acts_int8,
+        ...                      data1_label='FP32', data2_label='INT8',
+        ...                      display_figures=False)
+        >>> # Display or save figures as needed
+        >>> for i, fig in enumerate(figs):
+        ...     fig.savefig(f'comparison_{i}.png')
+        ...     plt.close(fig)
+    """
+    try:
+        import matplotlib.pyplot as plt
+    except ImportError:
+        msg = "matplotlib is required for visualization. Install with: pip install matplotlib"
+        raise ImportError(msg)
+
+    # Verify both datasets have the same layers
+    assert set(data1.keys()) == set(data2.keys()), "Activation datasets must contain the same layer names"
+
+    # Determine which activation types to process
+    if activation_type is None:
+        activation_types = ["in", "out"]
+    else:
+        activation_types = [activation_type]
+
+    all_figures = {}
+    summary_figures = {}
+    layer_names = list(data1.keys())
+
+    # Prepare data for summary plots (one per activation type)
+    summary_data = {}
+    for act_type in activation_types:
+        summary_data[act_type] = {
+            data1_label: {"mean": [], "std": []},
+            data2_label: {"mean": [], "std": []},
+            "diff": {"mean": [], "std": [], "relative": []},
+        }
+
+    # Loop over layers first, then activation types
+    for layer_name in layer_names:
+        all_figures[layer_name] = {}
+        for act_type in activation_types:
+            # Get activation arrays
+            data1_arrays = data1[layer_name][act_type]
+            data2_arrays = data2[layer_name][act_type]
+
+            # Flatten first, then concatenate (consistent with other methods)
+            data1_flat = np.concatenate([arr.flatten() for arr in data1_arrays])
+            data2_flat = np.concatenate([arr.flatten() for arr in data2_arrays])
+
+            # Show histogram for this layer and activation type
+            if show_histograms:
+                fig, ax = plt.subplots(1, figsize=(10, 6))
+                fig.suptitle(f"{layer_name} - {act_type}")
+
+                ax.hist(data1_flat, bins=bins, alpha=0.5, label=f"{data1_label}")
+                ax.hist(data2_flat, bins=bins, alpha=0.5, label=f"{data2_label}")
+                ax.set_xlabel("Activation Value")
+                ax.set_ylabel("Frequency")
+                ax.legend(loc="best")
+                ax.grid(True, alpha=0.3)
+
+                # Add statistics text
+                stats_text = (
+                    f"{data1_label}: min={data1_flat.min():.4f}, max={data1_flat.max():.4f}\n"
+                    f"{data2_label}: min={data2_flat.min():.4f}, max={data2_flat.max():.4f}"
+                )
+                ax.text(
+                    0.02,
+                    0.98,
+                    stats_text,
+                    transform=ax.transAxes,
+                    verticalalignment="top",
+                    fontsize=9,
+                    bbox=dict(boxstyle="round", facecolor="wheat", alpha=0.5),
+                )
+
+                all_figures[layer_name][act_type] = fig
+
+                # Close figure if not displaying to save memory
+                if not display_figures:
+                    plt.close(fig)
+
+            # Collect statistics for summary plot
+            plot_data = summary_data[act_type]
+            plot_data[data1_label]["mean"].append(np.mean(data1_flat))
+            plot_data[data2_label]["mean"].append(np.mean(data2_flat))
+            plot_data["diff"]["mean"].append(np.mean(data1_flat - data2_flat))
+            plot_data["diff"]["relative"].append(np.mean(np.abs(data1_flat - data2_flat) / (np.abs(data1_flat) + 1e-8)))
+
+            plot_data[data1_label]["std"].append(np.std(data1_flat))
+            plot_data[data2_label]["std"].append(np.std(data2_flat))
+            plot_data["diff"]["std"].append(np.std(data1_flat - data2_flat))
+
+    # Create summary plots (one for each activation type)
+    if show_summary:
+        for act_type in activation_types:
+            plot_data = summary_data[act_type]
+
+            fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(14, 16))
+            fig.suptitle(f"Activation Comparison Summary - {act_type}", fontsize=14)
+
+            x = range(len(layer_names))
+
+            # Plot 1: data1 mean and std
+            ax1.errorbar(
+                x,
+                plot_data[data1_label]["mean"],
+                plot_data[data1_label]["std"],
+                linestyle="none",
+                marker="^",
+                capsize=5,
+                **kwargs,
+            )
+            ax1.set_ylabel("Mean ± Std")
+            ax1.set_title(f"{data1_label} Activations")
+            ax1.set_xticks(x)
+            ax1.set_xticklabels([str(i) for i in x], fontsize=9)
+            ax1.grid(True, alpha=0.3)
+
+            # Plot 2: data2 mean and std
+            ax2.errorbar(
+                x,
+                plot_data[data2_label]["mean"],
+                plot_data[data2_label]["std"],
+                linestyle="none",
+                marker="^",
+                ecolor="green",
+                capsize=5,
+                **kwargs,
+            )
+            ax2.set_ylabel("Mean ± Std")
+            ax2.set_title(f"{data2_label} Activations")
+            ax2.set_xticks(x)
+            ax2.set_xticklabels([str(i) for i in x], fontsize=9)
+            ax2.grid(True, alpha=0.3)
+
+            # Plot 3: difference mean and std
+            ax3.errorbar(
+                x,
+                plot_data["diff"]["mean"],
+                plot_data["diff"]["std"],
+                linestyle="none",
+                marker="^",
+                ecolor="red",
+                capsize=5,
+                **kwargs,
+            )
+            ax3.set_ylabel("Mean ± Std")
+            ax3.set_title(f"{data1_label} - {data2_label} Difference")
+            ax3.set_xticks(x)
+            ax3.set_xticklabels([str(i) for i in x], fontsize=9)
+            ax3.grid(True, alpha=0.3)
+            ax3.axhline(y=0, color="k", linestyle="--", alpha=0.3)
+
+            # Plot 4: relative difference
+            ax4.plot(x, plot_data["diff"]["relative"], color="green", marker="o", **kwargs)
+            ax4.set_ylabel("Relative Difference")
+            ax4.set_title(f"{data1_label} - {data2_label} Relative Difference")
+            ax4.set_xlabel("Layer Index")
+            ax4.set_xticks(x)
+            ax4.set_xticklabels([str(i) for i in x], fontsize=9)
+            ax4.grid(True, alpha=0.3)
+
+            # Create legend with layer name mappings at the bottom
+            legend_text = "Layer Mapping:\n" + ", ".join([f"{i}: {name}" for i, name in enumerate(layer_names)])
+            fig.text(
+                0.5,
+                0.02,
+                legend_text,
+                ha="center",
+                va="top",
+                fontsize=12,
+                bbox=dict(boxstyle="round", facecolor="wheat", alpha=0.5),
+                wrap=True,
+            )
+
+            # Adjust layout to make room for legend
+            try:
+                plt.tight_layout(rect=[0, 0.08, 1, 0.97])
+            except Exception:
+                plt.tight_layout()
+
+            summary_figures[act_type] = fig
+
+            # Close figure if not displaying to save memory
+            if not display_figures:
+                plt.close(fig)
+        return all_figures, summary_figures
+    return all_figures
diff --git a/tools/profiler/tiny_llama_profiling.ipynb b/tools/profiler/tiny_llama_profiling.ipynb
new file mode 100644
index 00000000000..bda0595ff4b
--- /dev/null
+++ b/tools/profiler/tiny_llama_profiling.ipynb
@@ -0,0 +1,805 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce7a5ec6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright (c) 2025 Intel Corporation\n",
+    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "# you may not use this file except in compliance with the License.\n",
+    "# You may obtain a copy of the License at\n",
+    "#      http://www.apache.org/licenses/LICENSE-2.0\n",
+    "# Unless required by applicable law or agreed to in writing, software\n",
+    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "# See the License for the specific language governing permissions and\n",
+    "# limitations under the License."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc51bcfc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from functools import partial\n",
+    "\n",
+    "import numpy as np\n",
+    "from datasets import load_dataset\n",
+    "from optimum.intel.openvino import OVModelForCausalLM\n",
+    "from transformers import AutoTokenizer\n",
+    "\n",
+    "import nncf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "68552df7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODEL_ID = \"TinyLlama/TinyLlama-1.1B-Chat-v1.0\"\n",
+    "\n",
+    "dataset = load_dataset(\"wikitext\", \"wikitext-2-raw-v1\", split=\"test\")\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)\n",
+    "model = OVModelForCausalLM.from_pretrained(MODEL_ID, export=True, load_in_8bit=False, compile=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a6d227b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ov_model_fp = model.model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ac772b3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def transform_fn(data, tokenizer):\n",
+    "    tokenized_text = tokenizer(data[\"text\"], return_tensors=\"np\")\n",
+    "    input_ids = tokenized_text[\"input_ids\"]\n",
+    "    attention_mask = tokenized_text[\"attention_mask\"]\n",
+    "\n",
+    "    inputs = {}\n",
+    "    inputs[\"input_ids\"] = input_ids\n",
+    "    inputs[\"attention_mask\"] = tokenized_text[\"attention_mask\"]\n",
+    "    position_ids = np.cumsum(attention_mask, axis=1) - 1\n",
+    "    position_ids[attention_mask == 0] = 1\n",
+    "    inputs[\"position_ids\"] = position_ids\n",
+    "\n",
+    "    batch_size = input_ids.shape[0]\n",
+    "    inputs[\"beam_idx\"] = np.arange(batch_size, dtype=int)\n",
+    "\n",
+    "    return inputs\n",
+    "\n",
+    "\n",
+    "quantization_dataset = nncf.Dataset(dataset, partial(transform_fn, tokenizer=tokenizer))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c5855b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from profiler import NNCFProfiler\n",
+    "\n",
+    "profiler = NNCFProfiler(pattern = r'.*\\.self_attn\\.k_proj/ov_ext::linear/MatMul', \n",
+    "                        dataset=quantization_dataset, \n",
+    "                        num_samples=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72002051",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_fp = profiler.collect_activations(ov_model_fp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "36400530",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['__module.model.layers.0.self_attn.k_proj/ov_ext::linear/MatMul', '__module.model.layers.1.self_attn.k_proj/ov_ext::linear/MatMul', '__module.model.layers.2.self_attn.k_proj/ov_ext::linear/MatMul', '__module.model.layers.3.self_attn.k_proj/ov_ext::linear/MatMul', '__module.model.layers.4.self_attn.k_proj/ov_ext::linear/MatMul', '__module.model.layers.5.self_attn.k_proj/ov_ext::linear/MatMul', '__module.model.layers.6.self_attn.k_proj/ov_ext::linear/MatMul', '__module.model.layers.7.self_attn.k_proj/ov_ext::linear/MatMul', '__module.model.layers.8.self_attn.k_proj/ov_ext::linear/MatMul', '__module.model.layers.9.self_attn.k_proj/ov_ext::linear/MatMul', '__module.model.layers.10.self_attn.k_proj/ov_ext::linear/MatMul', '__module.model.layers.11.self_attn.k_proj/ov_ext::linear/MatMul', '__module.model.layers.12.self_attn.k_proj/ov_ext::linear/MatMul', '__module.model.layers.13.self_attn.k_proj/ov_ext::linear/MatMul', '__module.model.layers.14.self_attn.k_proj/ov_ext::linear/MatMul', '__module.model.layers.15.self_attn.k_proj/ov_ext::linear/MatMul', '__module.model.layers.16.self_attn.k_proj/ov_ext::linear/MatMul', '__module.model.layers.17.self_attn.k_proj/ov_ext::linear/MatMul', '__module.model.layers.18.self_attn.k_proj/ov_ext::linear/MatMul', '__module.model.layers.19.self_attn.k_proj/ov_ext::linear/MatMul', '__module.model.layers.20.self_attn.k_proj/ov_ext::linear/MatMul', '__module.model.layers.21.self_attn.k_proj/ov_ext::linear/MatMul'])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_fp.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "47a1bb08",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "    }\n",
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+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
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+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>type</th>\n",
+       "      <th>min</th>\n",
+       "      <th>max</th>\n",
+       "      <th>mean</th>\n",
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+       "      <td>-6.133480</td>\n",
+       "      <td>8.753073</td>\n",
+       "      <td>0.074276</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>__module.model.layers.2.self_attn.k_proj/ov_ex...</td>\n",
+       "      <td>in</td>\n",
+       "      <td>-7.766637</td>\n",
+       "      <td>3.266993</td>\n",
+       "      <td>-0.004558</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                name type        min  \\\n",
+       "0  __module.model.layers.0.self_attn.k_proj/ov_ex...   in  -3.702809   \n",
+       "1  __module.model.layers.0.self_attn.k_proj/ov_ex...  out -13.699491   \n",
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+       "3  __module.model.layers.1.self_attn.k_proj/ov_ex...  out  -6.133480   \n",
+       "4  __module.model.layers.2.self_attn.k_proj/ov_ex...   in  -7.766637   \n",
+       "\n",
+       "        max      mean  \n",
+       "0  4.830170  0.000816  \n",
+       "1  5.540934 -0.086840  \n",
+       "2  2.948636 -0.004756  \n",
+       "3  8.753073  0.074276  \n",
+       "4  3.266993 -0.004558  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats_fp = profiler.calculate_stats(data_fp, statistics=[\"min\", \"max\", \"mean\"])\n",
+    "stats_fp.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d7d0b634",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
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+       "      <th>type</th>\n",
+       "      <th>min</th>\n",
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+       "      <th>mean</th>\n",
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+       "    </tr>\n",
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+       "    <tr>\n",
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+       "      <td>0.000816</td>\n",
+       "      <td>0.011473</td>\n",
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+       "      <td>__module.model.layers.0.self_attn.k_proj/ov_ex...</td>\n",
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+       "      <td>8.753073</td>\n",
+       "      <td>0.074276</td>\n",
+       "      <td>3.285733</td>\n",
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+       "      <th>4</th>\n",
+       "      <td>__module.model.layers.2.self_attn.k_proj/ov_ex...</td>\n",
+       "      <td>in</td>\n",
+       "      <td>-7.766637</td>\n",
+       "      <td>3.266993</td>\n",
+       "      <td>-0.004558</td>\n",
+       "      <td>0.101055</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                name type        min  \\\n",
+       "0  __module.model.layers.0.self_attn.k_proj/ov_ex...   in  -3.702809   \n",
+       "1  __module.model.layers.0.self_attn.k_proj/ov_ex...  out -13.699491   \n",
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+       "\n",
+       "        max      mean  percentile_95  \n",
+       "0  4.830170  0.000816       0.011473  \n",
+       "1  5.540934 -0.086840       1.881651  \n",
+       "2  2.948636 -0.004756       0.050166  \n",
+       "3  8.753073  0.074276       3.285733  \n",
+       "4  3.266993 -0.004558       0.101055  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# It's possible to register your own statistics\n",
+    "\n",
+    "@NNCFProfiler.statistic(\"percentile_95\")\n",
+    "def _stat_percentile_95(vals: np.ndarray) -> float:\n",
+    "    \"\"\"Calculate the 95th percentile of activations.\"\"\"\n",
+    "    return float(np.percentile(vals, 95))\n",
+    "\n",
+    "\n",
+    "stats_fp = profiler.calculate_stats(data_fp, statistics=[\"min\", \"max\", \"mean\", \"percentile_95\"])\n",
+    "stats_fp.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "923ed700",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nncf import CompressWeightsMode\n",
+    "from nncf import compress_weights\n",
+    "\n",
+    "ov_model_int8 = compress_weights(ov_model_fp, mode=CompressWeightsMode.INT8_SYM, ratio=1.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d5ca5789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_int8 = profiler.collect_activations(ov_model_int8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bd62049",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>type</th>\n",
+       "      <th>min_data1</th>\n",
+       "      <th>max_data1</th>\n",
+       "      <th>mean_data1</th>\n",
+       "      <th>min_data2</th>\n",
+       "      <th>max_data2</th>\n",
+       "      <th>mean_data2</th>\n",
+       "      <th>mean_diff</th>\n",
+       "      <th>std_diff</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>__module.model.layers.0.self_attn.k_proj/ov_ex...</td>\n",
+       "      <td>in</td>\n",
+       "      <td>-3.702809</td>\n",
+       "      <td>4.830170</td>\n",
+       "      <td>0.000816</td>\n",
+       "      <td>-3.701592</td>\n",
+       "      <td>4.813562</td>\n",
+       "      <td>0.000813</td>\n",
+       "      <td>0.000003</td>\n",
+       "      <td>0.000686</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>__module.model.layers.0.self_attn.k_proj/ov_ex...</td>\n",
+       "      <td>out</td>\n",
+       "      <td>-13.699491</td>\n",
+       "      <td>5.540934</td>\n",
+       "      <td>-0.086840</td>\n",
+       "      <td>-13.664916</td>\n",
+       "      <td>5.459719</td>\n",
+       "      <td>-0.086497</td>\n",
+       "      <td>-0.000343</td>\n",
+       "      <td>0.007337</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>__module.model.layers.1.self_attn.k_proj/ov_ex...</td>\n",
+       "      <td>in</td>\n",
+       "      <td>-8.973475</td>\n",
+       "      <td>2.948636</td>\n",
+       "      <td>-0.004756</td>\n",
+       "      <td>-8.986747</td>\n",
+       "      <td>2.947640</td>\n",
+       "      <td>-0.004748</td>\n",
+       "      <td>-0.000008</td>\n",
+       "      <td>0.001551</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>__module.model.layers.1.self_attn.k_proj/ov_ex...</td>\n",
+       "      <td>out</td>\n",
+       "      <td>-6.133480</td>\n",
+       "      <td>8.753073</td>\n",
+       "      <td>0.074276</td>\n",
+       "      <td>-6.118840</td>\n",
+       "      <td>8.763062</td>\n",
+       "      <td>0.074142</td>\n",
+       "      <td>0.000134</td>\n",
+       "      <td>0.011611</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>__module.model.layers.2.self_attn.k_proj/ov_ex...</td>\n",
+       "      <td>in</td>\n",
+       "      <td>-7.766637</td>\n",
+       "      <td>3.266993</td>\n",
+       "      <td>-0.004558</td>\n",
+       "      <td>-7.765649</td>\n",
+       "      <td>3.290504</td>\n",
+       "      <td>-0.004616</td>\n",
+       "      <td>0.000058</td>\n",
+       "      <td>0.003471</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                name type  min_data1  \\\n",
+       "0  __module.model.layers.0.self_attn.k_proj/ov_ex...   in  -3.702809   \n",
+       "1  __module.model.layers.0.self_attn.k_proj/ov_ex...  out -13.699491   \n",
+       "2  __module.model.layers.1.self_attn.k_proj/ov_ex...   in  -8.973475   \n",
+       "3  __module.model.layers.1.self_attn.k_proj/ov_ex...  out  -6.133480   \n",
+       "4  __module.model.layers.2.self_attn.k_proj/ov_ex...   in  -7.766637   \n",
+       "\n",
+       "   max_data1  mean_data1  min_data2  max_data2  mean_data2 mean_diff  std_diff  \n",
+       "0   4.830170    0.000816  -3.701592   4.813562    0.000813  0.000003  0.000686  \n",
+       "1   5.540934   -0.086840 -13.664916   5.459719   -0.086497 -0.000343  0.007337  \n",
+       "2   2.948636   -0.004756  -8.986747   2.947640   -0.004748 -0.000008  0.001551  \n",
+       "3   8.753073    0.074276  -6.118840   8.763062    0.074142  0.000134  0.011611  \n",
+       "4   3.266993   -0.004558  -7.765649   3.290504   -0.004616  0.000058  0.003471  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "comp_res = profiler.compare_activations(data_fp, \n",
+    "                                        data_int8, \n",
+    "                                        metrics=[\"mean_diff\", \"std_diff\"], \n",
+    "                                        statistics = [\"min\", \"max\", \"mean\"])\n",
+    "comp_res.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e2e5356",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You can register your own function for comparing statistics\n",
+    "\n",
+    "@NNCFProfiler.comparator(\"relative_diff\")\n",
+    "def _relative_diff(a: np.ndarray, b: np.ndarray) -> float:\n",
+    "    \"\"\"Calculate the mean relative difference between two activation arrays.\"\"\"\n",
+    "    epsilon = 1e-8\n",
+    "    return float((np.abs(a - b) / (np.abs(a) + epsilon)).mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b0e8a19",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>type</th>\n",
+       "      <th>min_data1</th>\n",
+       "      <th>max_data1</th>\n",
+       "      <th>mean_data1</th>\n",
+       "      <th>min_data2</th>\n",
+       "      <th>max_data2</th>\n",
+       "      <th>mean_data2</th>\n",
+       "      <th>mean_diff</th>\n",
+       "      <th>std_diff</th>\n",
+       "      <th>relative_diff</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>__module.model.layers.0.self_attn.k_proj/ov_ex...</td>\n",
+       "      <td>in</td>\n",
+       "      <td>-3.702809</td>\n",
+       "      <td>4.830170</td>\n",
+       "      <td>0.000816</td>\n",
+       "      <td>-3.701592</td>\n",
+       "      <td>4.813562</td>\n",
+       "      <td>0.000813</td>\n",
+       "      <td>0.000003</td>\n",
+       "      <td>0.000686</td>\n",
+       "      <td>0.083059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>__module.model.layers.0.self_attn.k_proj/ov_ex...</td>\n",
+       "      <td>out</td>\n",
+       "      <td>-13.699491</td>\n",
+       "      <td>5.540934</td>\n",
+       "      <td>-0.086840</td>\n",
+       "      <td>-13.664916</td>\n",
+       "      <td>5.459719</td>\n",
+       "      <td>-0.086497</td>\n",
+       "      <td>-0.000343</td>\n",
+       "      <td>0.007337</td>\n",
+       "      <td>0.038519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>__module.model.layers.1.self_attn.k_proj/ov_ex...</td>\n",
+       "      <td>in</td>\n",
+       "      <td>-8.973475</td>\n",
+       "      <td>2.948636</td>\n",
+       "      <td>-0.004756</td>\n",
+       "      <td>-8.986747</td>\n",
+       "      <td>2.947640</td>\n",
+       "      <td>-0.004748</td>\n",
+       "      <td>-0.000008</td>\n",
+       "      <td>0.001551</td>\n",
+       "      <td>0.16398</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>__module.model.layers.1.self_attn.k_proj/ov_ex...</td>\n",
+       "      <td>out</td>\n",
+       "      <td>-6.133480</td>\n",
+       "      <td>8.753073</td>\n",
+       "      <td>0.074276</td>\n",
+       "      <td>-6.118840</td>\n",
+       "      <td>8.763062</td>\n",
+       "      <td>0.074142</td>\n",
+       "      <td>0.000134</td>\n",
+       "      <td>0.011611</td>\n",
+       "      <td>0.026449</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>__module.model.layers.2.self_attn.k_proj/ov_ex...</td>\n",
+       "      <td>in</td>\n",
+       "      <td>-7.766637</td>\n",
+       "      <td>3.266993</td>\n",
+       "      <td>-0.004558</td>\n",
+       "      <td>-7.765649</td>\n",
+       "      <td>3.290504</td>\n",
+       "      <td>-0.004616</td>\n",
+       "      <td>0.000058</td>\n",
+       "      <td>0.003471</td>\n",
+       "      <td>0.220715</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                name type  min_data1  \\\n",
+       "0  __module.model.layers.0.self_attn.k_proj/ov_ex...   in  -3.702809   \n",
+       "1  __module.model.layers.0.self_attn.k_proj/ov_ex...  out -13.699491   \n",
+       "2  __module.model.layers.1.self_attn.k_proj/ov_ex...   in  -8.973475   \n",
+       "3  __module.model.layers.1.self_attn.k_proj/ov_ex...  out  -6.133480   \n",
+       "4  __module.model.layers.2.self_attn.k_proj/ov_ex...   in  -7.766637   \n",
+       "\n",
+       "   max_data1  mean_data1  min_data2  max_data2  mean_data2 mean_diff  \\\n",
+       "0   4.830170    0.000816  -3.701592   4.813562    0.000813  0.000003   \n",
+       "1   5.540934   -0.086840 -13.664916   5.459719   -0.086497 -0.000343   \n",
+       "2   2.948636   -0.004756  -8.986747   2.947640   -0.004748 -0.000008   \n",
+       "3   8.753073    0.074276  -6.118840   8.763062    0.074142  0.000134   \n",
+       "4   3.266993   -0.004558  -7.765649   3.290504   -0.004616  0.000058   \n",
+       "\n",
+       "   std_diff relative_diff  \n",
+       "0  0.000686      0.083059  \n",
+       "1  0.007337      0.038519  \n",
+       "2  0.001551       0.16398  \n",
+       "3  0.011611      0.026449  \n",
+       "4  0.003471      0.220715  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "comp_res = profiler.compare_activations(data_fp, \n",
+    "                                        data_int8, \n",
+    "                                        metrics=[\"mean_diff\", \"std_diff\", \"relative_diff\"], \n",
+    "                                        statistics = [\"min\", \"max\", \"mean\"])\n",
+    "comp_res.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a22d27e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# It's also possible to register your own visualizer (see the implemenation in nncf/tools/profiler.py)\n",
+    "\n",
+    "# @NNCFProfiler.visualizer(\"compare_detailed\")\n",
+    "# def _compare_detailed_plot(\n",
+    "#     data1: ActivationData,\n",
+    "#     data2: ActivationData,\n",
+    "#     activation_type: Optional[str] = None,\n",
+    "#     bins: int = 100,\n",
+    "#     show_histograms: bool = True,\n",
+    "#     show_summary: bool = True,\n",
+    "#     display_figures: bool = False,\n",
+    "#     data1_label: str = 'data1',\n",
+    "#     data2_label: str = 'data2',\n",
+    "#     **kwargs\n",
+    "# ): "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "67f76682",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_figs, summary_figs = profiler.plot(\"compare_detailed\", \n",
+    "                                       data_fp, \n",
+    "                                       data_int8, \n",
+    "                                       data1_label='data_fp', \n",
+    "                                       data2_label = 'data_int8', \n",
+    "                                       display_figures=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "beb812a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import display\n",
+    "\n",
+    "display(all_figs['__module.model.layers.0.self_attn.k_proj/ov_ext::linear/MatMul']['out'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "187cdfe0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x1600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(summary_figs['in'])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nncf-build",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}