diff --git a/src/pruna/algorithms/quantization/torch_static.py b/src/pruna/algorithms/quantization/torch_static.py
deleted file mode 100644
index cb7cfce0..00000000
--- a/src/pruna/algorithms/quantization/torch_static.py
+++ /dev/null
@@ -1,193 +0,0 @@
-# Copyright 2025 - Pruna AI GmbH. All rights reserved.
-#
-# 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.
-
-from typing import Any, Dict
-
-import torch
-from ConfigSpace import CategoricalHyperparameter, Constant, OrdinalHyperparameter
-
-from pruna.algorithms.quantization import PrunaQuantizer
-from pruna.config.smash_config import SmashConfigPrefixWrapper
-from pruna.data.utils import wrap_batch_for_model_call
-from pruna.engine.save import SAVE_FUNCTIONS
-
-
-class TorchStaticQuantizer(PrunaQuantizer):
-    """
-    Implement static quantization using torch.
-
-    In static quantization, both weights and activations are pre-converted to lower precision (e.g., int8)
-    using a calibration process on representative data, which typically yields greater efficiency gains but
-    requires additional steps during model preparation.
-    """
-
-    algorithm_name = "torch_static"
-    references = {"GitHub": "https://github.com/pytorch/pytorch"}
-    save_fn = SAVE_FUNCTIONS.pickled
-    tokenizer_required = False
-    processor_required = False
-    run_on_cpu = True
-    run_on_cuda = True
-    dataset_required = True
-    compatible_algorithms = dict()
-
-    def get_hyperparameters(self) -> list:
-        """
-        Configure all algorithm-specific hyperparameters with ConfigSpace.
-
-        Returns
-        -------
-        list
-            The hyperparameters.
-        """
-        return [
-            OrdinalHyperparameter(
-                "weight_bits",
-                sequence=["quint8", "qint8"],
-                default_value="qint8",
-                meta=dict(desc="Tensor type to use for weight quantization."),
-            ),
-            OrdinalHyperparameter(
-                "act_bits",
-                sequence=["quint8", "qint8"],
-                default_value="qint8",
-                meta=dict(desc="Tensor type to use for activation quantization."),
-            ),
-            CategoricalHyperparameter(
-                "qscheme",
-                choices=["per_tensor_symmetric", "per_tensor_affine"],
-                default_value="per_tensor_affine",
-                meta=dict(desc="Quantization scheme to use."),
-            ),
-            CategoricalHyperparameter(
-                "qobserver",
-                choices=[
-                    "MinMaxObserver",
-                    "MovingAverageMinMaxObserver",
-                    "PerChannelMinMaxObserver",
-                    "HistogramObserver",
-                ],
-                default_value="MinMaxObserver",
-                meta=dict(desc="Observer to use for quantization."),
-            ),
-            Constant(
-                name="calibration_samples",
-                value=16,
-                meta=dict(desc="Number of samples to use for calibration."),
-            ),
-        ]
-
-    def model_check_fn(self, model: Any) -> bool:
-        """
-        Check if the model is supported.
-
-        Parameters
-        ----------
-        model : Any
-            The model to check.
-
-        Returns
-        -------
-        bool
-            True if the model is supported, False otherwise.
-        """
-        return isinstance(model, torch.nn.Module)
-
-    def _apply(self, model: Any, smash_config: SmashConfigPrefixWrapper) -> Any:
-        """
-        Quantize the model with torch static quantization.
-
-        Parameters
-        ----------
-        model : Any
-            The model to quantize.
-        smash_config : SmashConfigPrefixWrapper
-            The configuration for the quantization.
-
-        Returns
-        -------
-        Any
-            The quantized model.
-        """
-        qtype_weight = getattr(torch, smash_config["weight_bits"])
-        qtype_activation = getattr(torch, smash_config["act_bits"])
-        qscheme = getattr(torch, smash_config["qscheme"])
-        qobserver = getattr(torch.quantization, smash_config["qobserver"])
-        model.eval().to("cpu")
-        qconfig = torch.quantization.QConfig(
-            activation=qobserver.with_args(dtype=qtype_activation, qscheme=qscheme),
-            weight=qobserver.with_args(dtype=qtype_weight, qscheme=qscheme),
-        )
-        quantized_model = QuantWrapper(model, qconfig)
-        torch.ao.quantization.prepare(quantized_model, inplace=True)
-
-        # dataloader has been ensured to be set in the config
-        for i, batch in enumerate(smash_config.train_dataloader()):  # type: ignore[arg-type]
-            if i >= smash_config["calibration_samples"]:
-                break
-            wrap_batch_for_model_call(batch, quantized_model, device="cpu")
-
-        torch.ao.quantization.convert(quantized_model, inplace=True)
-
-        return quantized_model
-
-    def import_algorithm_packages(self) -> Dict[str, Any]:
-        """
-        Provide a algorithm packages for the algorithm.
-
-        Returns
-        -------
-        Dict[str, Any]
-            The algorithm packages.
-        """
-        return dict()
-
-
-class QuantWrapper(torch.nn.Module):
-    """
-    A wrapper for quantizing a PyTorch model.
-
-    Parameters
-    ----------
-    model : torch.nn.Module
-        The model to quantize.
-    qconfig : torch.quantization.QConfig
-        The quantization configuration.
-    """
-
-    def __init__(self, model: torch.nn.Module, qconfig: torch.quantization.QConfig) -> None:
-        super().__init__()
-        self.model = model
-        self.quant = torch.quantization.QuantStub()
-        self.dequant = torch.quantization.DeQuantStub()
-        self.qconfig = qconfig
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """
-        Forward pass for the quantization wrapper.
-
-        Parameters
-        ----------
-        x : torch.Tensor
-            Input tensor to be quantized and passed through the model.
-
-        Returns
-        -------
-        torch.Tensor
-            Dequantized output tensor from the model.
-        """
-        x = self.quant(x)
-        x = self.model(x)
-        x = self.dequant(x)
-        return x
diff --git a/tests/algorithms/testers/quantization.py b/tests/algorithms/testers/quantization.py
index b0ccb8b8..1e7534e4 100644
--- a/tests/algorithms/testers/quantization.py
+++ b/tests/algorithms/testers/quantization.py
@@ -12,21 +12,11 @@
 from pruna.algorithms.quantization.huggingface_llm_int8 import LLMInt8Quantizer
 from pruna.algorithms.quantization.quanto import QuantoQuantizer
 from pruna.algorithms.quantization.torch_dynamic import TorchDynamicQuantizer
-from pruna.algorithms.quantization.torch_static import TorchStaticQuantizer
 from pruna.algorithms.quantization.torchao import TorchaoQuantizer
 
 from .base_tester import AlgorithmTesterBase
 
 
-class TestTorchStatic(AlgorithmTesterBase):
-    """Test the torch static quantizer."""
-
-    models = ["noref_mobilenet_v2"]
-    reject_models = []
-    allow_pickle_files = False
-    algorithm_class = TorchStaticQuantizer
-
-
 class TestTorchDynamic(AlgorithmTesterBase):
     """Test the torch dynamic quantizer."""