diff --git a/.github/actionlint.yaml b/.github/actionlint.yaml
index ca599dcb832..e9d56f40cc8 100644
--- a/.github/actionlint.yaml
+++ b/.github/actionlint.yaml
@@ -3,3 +3,5 @@ self-hosted-runner:
     - windows-2025-8-core
     - windows-2025-16-core
     - aks-linux-6-cores-55gb-gpu-a10
+    - Linux
+    - NNCF
diff --git a/.github/workflows/call_precommit.yml b/.github/workflows/call_precommit.yml
index acea16b1670..2c5787819b2 100644
--- a/.github/workflows/call_precommit.yml
+++ b/.github/workflows/call_precommit.yml
@@ -19,158 +19,37 @@ on:
         required: false
 
 jobs:
-  common:
-    timeout-minutes: 40
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        shell: bash
-    steps:
-      - uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7.0.0
-        with:
-          lfs: true
-      - uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6.3.0
-        with:
-          python-version: ${{ inputs.python_version }}
-      - name: Install uv
-        uses: astral-sh/setup-uv@fac544c07dec837d0ccb6301d7b5580bf5edae39 # v8.2.0
-      - name: Override constraints
-        if: ${{ inputs.override_requirements != '' }}
-        run: python .github/scripts/override_constraints.py "${{ inputs.override_requirements }}"
-        shell: bash
-      - name: Install NNCF and test requirements
-        run: uv pip install --system . -r tests/common/requirements.txt
-      - name: Print installed modules
-        run: pip list
-      - name: Run common precommit test scope
-        run: make test-common
-        env:
-          NUM_WORKERS: 2
-
-  onnx:
-    timeout-minutes: 40
-    runs-on: ubuntu-latest-8-cores
-    defaults:
-      run:
-        shell: bash
-    steps:
-      - uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7.0.0
-        with:
-          lfs: true
-      - uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6.3.0
-        with:
-          python-version: ${{ inputs.python_version }}
-      - name: Install uv
-        uses: astral-sh/setup-uv@fac544c07dec837d0ccb6301d7b5580bf5edae39 # v8.2.0
-      - name: Override constraints
-        if: ${{ inputs.override_requirements != '' }}
-        run: python .github/scripts/override_constraints.py "${{ inputs.override_requirements }}"
-        shell: bash
-      - name: Install NNCF and test requirements
-        run: uv pip install --system . -r tests/onnx/requirements.txt
-      - name: Print installed modules
-        run: pip list
-      - name: Run ONNX precommit test scope
-        run: make test-onnx
-        env:
-          NUM_WORKERS: 4
-
-  openvino:
-    timeout-minutes: 40
-    runs-on: ubuntu-latest-8-cores
-    defaults:
-      run:
-        shell: bash
-    steps:
-      - uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7.0.0
-        with:
-          lfs: true
-      - uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6.3.0
-        with:
-          python-version: ${{ inputs.python_version }}
-      - name: Install uv
-        uses: astral-sh/setup-uv@fac544c07dec837d0ccb6301d7b5580bf5edae39 # v8.2.0
-      - name: Override constraints
-        if: ${{ inputs.override_requirements != '' }}
-        run: python .github/scripts/override_constraints.py "${{ inputs.override_requirements }}"
-        shell: bash
-      - name: Install NNCF and test requirements
-        run: uv pip install --system . -r tests/openvino/requirements.txt
-      - name: Print installed modules
-        run: pip list
-      - name: Run OV precommit test scope
-        run: make test-openvino
-        env:
-          NUM_WORKERS: 4
-
-  tools:
-    timeout-minutes: 40
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        shell: bash
-    steps:
-      - uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7.0.0
-        with:
-          lfs: true
-      - uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6.3.0
-        with:
-          python-version: ${{ inputs.python_version }}
-      - name: Install uv
-        uses: astral-sh/setup-uv@fac544c07dec837d0ccb6301d7b5580bf5edae39 # v8.2.0
-      - name: Install test requirements
-        run: uv pip install --system . -r tests/tools/requirements.txt
-      - name: Print installed modules
-        run: pip list
-      - name: Run tools precommit test scope
-        run: pytest -ra tests/tools
-
-  pytorch-cpu:
-    timeout-minutes: 40
-    runs-on: ubuntu-latest
-    # TODO(AlexanderDokuchaev): Enable with torchao=0.18.0 release
-    if: ${{ inputs.python_version != '3.14' }}
-    defaults:
-      run:
-        shell: bash
-    steps:
-      - uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7.0.0
-        with:
-          lfs: true
-      - uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6.3.0
-        with:
-          python-version: ${{ inputs.python_version }}
-      - name: Install uv
-        uses: astral-sh/setup-uv@fac544c07dec837d0ccb6301d7b5580bf5edae39 # v8.2.0
-      - name: Override constraints
-        if: ${{ inputs.override_requirements != '' }}
-        run: python .github/scripts/override_constraints.py "${{ inputs.override_requirements }}"
-        shell: bash
-      - name: Install NNCF and test requirements
-        run: uv pip install --system . -r tests/torch/requirements.txt
-      - name: Print installed modules
-        run: pip list
-      - name: Run torch precommit test scope
-        run: pytest -ra -n2 --durations=30 tests/torch -m "not cuda"
-
   pytorch-cuda:
     timeout-minutes: 40
-    runs-on: aks-linux-6-cores-55gb-gpu-a10
+    runs-on:
+      group: Nvidia-GPU
+      labels: [Linux, NNCF]
     if: ${{ inputs.gpu_enabled == true }}
+    container:
+      image: ubuntu:24.04
+      options: --gpus all --ipc=host
+      volumes:
+        - /usr/local/share/ca-certificates:/usr/local/share/ca-certificates:ro
     defaults:
       run:
         shell: bash
     env:
       DEBIAN_FRONTEND: noninteractive
+      NODE_EXTRA_CA_CERTS: /usr/local/share/ca-certificates/IntelProxyRootCA-Base64.crt
+      GIT_SSL_CAINFO: /usr/local/share/ca-certificates/IntelProxyRootCA-Base64.crt
+      PIP_CERT: /usr/local/share/ca-certificates/IntelProxyRootCA-Base64.crt
+      REQUESTS_CA_BUNDLE: /usr/local/share/ca-certificates/IntelProxyRootCA-Base64.crt
+      SSL_CERT_FILE: /etc/ssl/certs/ca-certificates.crt
     steps:
       - name: Install dependencies
         run : |
-          sudo apt-get update
-          sudo apt-get --assume-yes install build-essential ninja-build libgl1-mesa-dev libglib2.0-0 wget make
+          apt-get update
+          apt-get --assume-yes install git git-lfs ca-certificates build-essential ninja-build libgl1-mesa-dev libglib2.0-0 wget make
+          update-ca-certificates
       - name: Download CUDA
         run: |
-          wget -q https://developer.download.nvidia.com/compute/cuda/12.6.3/local_installers/cuda_12.6.3_560.35.05_linux.run
-          sudo sh cuda_12.6.3_560.35.05_linux.run --toolkit --silent
+          wget -q https://developer.download.nvidia.com/compute/cuda/13.2.1/local_installers/cuda_13.2.1_595.58.03_linux.run
+          sh cuda_13.2.1_595.58.03_linux.run --toolkit --silent
       - name: Runner info
         continue-on-error: true
         run: |
@@ -182,14 +61,16 @@ jobs:
       - uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6.3.0
         with:
           python-version: ${{ inputs.python_version }}
-      - name: Install uv
-        uses: astral-sh/setup-uv@fac544c07dec837d0ccb6301d7b5580bf5edae39 # v8.2.0
+      # - name: Install uv
+      #   uses: astral-sh/setup-uv@fac544c07dec837d0ccb6301d7b5580bf5edae39 # v8.2.0
       - name: Override constraints
         if: ${{ inputs.override_requirements != '' }}
         run: python .github/scripts/override_constraints.py "${{ inputs.override_requirements }}"
         shell: bash
+      - name: Print installed modules
+        run: pip list
       - name: Install NNCF and test requirements
-        run: uv pip install --system . -r tests/torch/requirements.txt
+        run: pip install . -r tests/torch/requirements.txt
       - name: Print installed modules
         run: pip list
       - name: Check CUDA
diff --git a/.github/workflows/examples.yml b/.github/workflows/examples.yml
index ee7a6ae5143..957320698b2 100644
--- a/.github/workflows/examples.yml
+++ b/.github/workflows/examples.yml
@@ -110,8 +110,8 @@ jobs:
           sudo apt-get --assume-yes install build-essential ninja-build libgl1-mesa-dev libglib2.0-0 wget make
       - name: Download CUDA
         run: |
-          wget -q https://developer.download.nvidia.com/compute/cuda/12.6.3/local_installers/cuda_12.6.3_560.35.05_linux.run
-          sudo sh cuda_12.6.3_560.35.05_linux.run --toolkit --silent
+          wget -q https://developer.download.nvidia.com/compute/cuda/13.2.1/local_installers/cuda_13.2.1_595.58.03_linux.run
+          sudo sh cuda_13.2.1_595.58.03_linux.run --toolkit --silent
       - name: Runner info
         continue-on-error: true
         run: |
diff --git a/.github/workflows/install.yml b/.github/workflows/install.yml
index 9d84c476651..487a341e839 100644
--- a/.github/workflows/install.yml
+++ b/.github/workflows/install.yml
@@ -59,8 +59,8 @@ jobs:
           sudo apt-get --assume-yes install build-essential ninja-build libgl1-mesa-dev libglib2.0-0 wget make virtualenv
       - name: Download CUDA
         run: |
-          wget -q https://developer.download.nvidia.com/compute/cuda/12.6.3/local_installers/cuda_12.6.3_560.35.05_linux.run
-          sudo sh cuda_12.6.3_560.35.05_linux.run --toolkit --silent
+          wget -q https://developer.download.nvidia.com/compute/cuda/13.2.1/local_installers/cuda_13.2.1_595.58.03_linux.run
+          sudo sh cuda_13.2.1_595.58.03_linux.run --toolkit --silent
       - name: Runner info
         continue-on-error: true
         run: |
diff --git a/README.md b/README.md
index 517ce94efee..3c6f22d7623 100644
--- a/README.md
+++ b/README.md
@@ -163,8 +163,8 @@ def transform_fn(data_item):
 calibration_dataset = nncf.Dataset(dataset_loader, transform_fn)
 
 # Step 3: Export model to TorchFX
-input_shape = (1, 3, 224, 224)
-fx_model = torch.export.export_for_training(model, args=(ex_input,)).module()
+ex_input = torch.randn(1, 3, 224, 224)
+fx_model = torch.export.export(model, args=(ex_input,)).module()
 # or
 # fx_model = torch.export.export(model, args=(ex_input,)).module()
 
diff --git a/constraints.txt b/constraints.txt
index b3d401117f5..5eba49ebf90 100644
--- a/constraints.txt
+++ b/constraints.txt
@@ -2,16 +2,16 @@
 openvino==2026.2.1
 
 # Pytorch
-torch==2.10.0
-torchvision==0.25.0
+torch==2.11.0
+torchvision==0.26.0
 torchao==0.17.0
 
 # ONNX
 onnx==1.22.0
-onnx-ir==0.1.15
+onnx-ir==0.2.1
 onnxruntime==1.23.2; python_version < '3.11'
 onnxruntime==1.24.3; python_version >= '3.11'
-onnxscript==0.6.2
+onnxscript==0.7.0
 
 # Tests and examples
 pytest==9.0.3
diff --git a/docs/Installation.md b/docs/Installation.md
index 8cb6e08f2d1..66e425fc156 100644
--- a/docs/Installation.md
+++ b/docs/Installation.md
@@ -48,7 +48,7 @@ as well as the supported versions of Python:
 
 | NNCF      | OpenVINO   | PyTorch  | ONNX     | TensorFlow | Python |
 |-----------|------------|----------|----------|------------|--------|
-| `develop` | `2026.2.1` | `2.10.0` | `1.22.0` |            | `3.12` |
+| `develop` | `2026.2.1` | `2.11.0` | `1.22.0` |            | `3.12` |
 | `3.2.0`   | `2026.2.0` | `2.10.0` | `1.21.0` |            | `3.12` |
 | `3.1.0`   | `2026.1.0` | `2.10.0` | `1.21.0` |            | `3.10` |
 | `3.0.0`   | `2026.0.0` | `2.9.0`  | `1.17.0` |            | `3.10` |
@@ -73,4 +73,4 @@ as well as the supported versions of Python:
 
 > (*) Python 3.9 or higher is required for TensorFlow 2.15.1
 
-This repository is tested on Python* 3.12, PyTorch* 2.10.0 (NVidia CUDA\* Toolkit 12.6).
+This repository is tested on Python* 3.12, PyTorch* 2.11.0 (NVidia CUDA\* Toolkit 13.2).
diff --git a/examples/llm_compression/onnx/tiny_llama/requirements.txt b/examples/llm_compression/onnx/tiny_llama/requirements.txt
index 5b078595120..91b972c7885 100644
--- a/examples/llm_compression/onnx/tiny_llama/requirements.txt
+++ b/examples/llm_compression/onnx/tiny_llama/requirements.txt
@@ -6,4 +6,4 @@ optimum-onnx @ git+https://github.com/AlexanderDokuchaev/optimum-onnx.git@b57739
 onnx==1.22.0
 onnxruntime==1.23.2; python_version < '3.11'
 onnxruntime==1.24.3; python_version >= '3.11'
-torch==2.10.0
+torch==2.11.0
diff --git a/examples/llm_compression/onnx/tiny_llama_scale_estimation/requirements.txt b/examples/llm_compression/onnx/tiny_llama_scale_estimation/requirements.txt
index 9c2d010b29e..89bf1eb23ae 100644
--- a/examples/llm_compression/onnx/tiny_llama_scale_estimation/requirements.txt
+++ b/examples/llm_compression/onnx/tiny_llama_scale_estimation/requirements.txt
@@ -1,4 +1,4 @@
-torch==2.10.0
+torch==2.11.0
 transformers==5.0.0
 openvino==2026.2.1
 optimum-intel[openvino]==2.0.0
diff --git a/examples/llm_compression/openvino/smollm2_360m_adaptive_codebook/requirements.txt b/examples/llm_compression/openvino/smollm2_360m_adaptive_codebook/requirements.txt
index 89c81a4412f..c0b2c66fcf0 100644
--- a/examples/llm_compression/openvino/smollm2_360m_adaptive_codebook/requirements.txt
+++ b/examples/llm_compression/openvino/smollm2_360m_adaptive_codebook/requirements.txt
@@ -4,5 +4,5 @@ optimum-intel[openvino]==2.0.0
 optimum==2.2.0
 transformers==5.0.0
 onnx==1.22.0
-torch==2.10.0
-torchvision==0.25.0
+torch==2.11.0
+torchvision==0.26.0
diff --git a/examples/llm_compression/openvino/smollm2_360m_codebook/requirements.txt b/examples/llm_compression/openvino/smollm2_360m_codebook/requirements.txt
index de2619dc081..9df341c1426 100644
--- a/examples/llm_compression/openvino/smollm2_360m_codebook/requirements.txt
+++ b/examples/llm_compression/openvino/smollm2_360m_codebook/requirements.txt
@@ -3,5 +3,5 @@ optimum-intel[openvino]==2.0.0
 optimum==2.2.0
 transformers==5.0.0
 onnx==1.22.0
-torch==2.10.0
-torchvision==0.25.0
+torch==2.11.0
+torchvision==0.26.0
diff --git a/examples/llm_compression/openvino/smollm2_360m_fp8/requirements.txt b/examples/llm_compression/openvino/smollm2_360m_fp8/requirements.txt
index 7e594ef1ee2..f3468d06ec9 100644
--- a/examples/llm_compression/openvino/smollm2_360m_fp8/requirements.txt
+++ b/examples/llm_compression/openvino/smollm2_360m_fp8/requirements.txt
@@ -4,4 +4,4 @@ optimum-intel[openvino]==2.0.0
 optimum==2.2.0
 transformers==5.0.0
 onnx==1.22.0
-torch==2.10.0
+torch==2.11.0
diff --git a/examples/llm_compression/openvino/tiny_llama/requirements.txt b/examples/llm_compression/openvino/tiny_llama/requirements.txt
index 509e795236e..7f494f11c17 100644
--- a/examples/llm_compression/openvino/tiny_llama/requirements.txt
+++ b/examples/llm_compression/openvino/tiny_llama/requirements.txt
@@ -3,5 +3,5 @@ onnx==1.22.0
 openvino==2026.2.1
 optimum-intel[openvino]==2.0.0
 optimum==2.2.0
-torch==2.10.0
+torch==2.11.0
 transformers==5.0.0
diff --git a/examples/llm_compression/openvino/tiny_llama_find_hyperparams/requirements.txt b/examples/llm_compression/openvino/tiny_llama_find_hyperparams/requirements.txt
index 69e59bd5b98..23a6caeef41 100644
--- a/examples/llm_compression/openvino/tiny_llama_find_hyperparams/requirements.txt
+++ b/examples/llm_compression/openvino/tiny_llama_find_hyperparams/requirements.txt
@@ -5,4 +5,4 @@ optimum-intel==2.0.0
 optimum==2.2.0
 transformers==5.0.0
 onnx==1.22.0
-torch==2.10.0
+torch==2.11.0
diff --git a/examples/llm_compression/openvino/tiny_llama_synthetic_data/requirements.txt b/examples/llm_compression/openvino/tiny_llama_synthetic_data/requirements.txt
index 90a653476ce..55dbb118eb3 100644
--- a/examples/llm_compression/openvino/tiny_llama_synthetic_data/requirements.txt
+++ b/examples/llm_compression/openvino/tiny_llama_synthetic_data/requirements.txt
@@ -1,4 +1,4 @@
-torch==2.10.0
+torch==2.11.0
 datasets==5.0.0
 numpy>=1.23.5,<2
 openvino==2026.2.1
diff --git a/examples/llm_compression/torch/distillation_qat_with_lora/requirements.txt b/examples/llm_compression/torch/distillation_qat_with_lora/requirements.txt
index d1294416579..1feadbe82d3 100644
--- a/examples/llm_compression/torch/distillation_qat_with_lora/requirements.txt
+++ b/examples/llm_compression/torch/distillation_qat_with_lora/requirements.txt
@@ -1,5 +1,5 @@
 tensorboard==2.20.0
-torch==2.10.0
+torch==2.11.0
 torchao==0.17.0
 numpy>=1.23.5,<2
 openvino==2026.2.1
diff --git a/examples/llm_compression/torch/downstream_qat_with_nls/requirements.txt b/examples/llm_compression/torch/downstream_qat_with_nls/requirements.txt
index b8fde577db6..8273383b855 100644
--- a/examples/llm_compression/torch/downstream_qat_with_nls/requirements.txt
+++ b/examples/llm_compression/torch/downstream_qat_with_nls/requirements.txt
@@ -1,5 +1,5 @@
 tensorboard==2.20.0
-torch==2.10.0
+torch==2.11.0
 numpy>=1.23.5,<2
 openvino==2026.2.1
 optimum-intel==2.0.0
diff --git a/examples/llm_compression/torch_fx/tiny_llama/modelling.py b/examples/llm_compression/torch_fx/tiny_llama/modelling.py
index d5cfeeb1e76..e03496d83ef 100644
--- a/examples/llm_compression/torch_fx/tiny_llama/modelling.py
+++ b/examples/llm_compression/torch_fx/tiny_llama/modelling.py
@@ -106,7 +106,7 @@ def convert_and_export_with_cache(model: PreTrainedModel) -> tuple[ExportedProgr
 
     dynamic_shapes = {"input_ids": {1: torch.export.Dim.DYNAMIC}, "cache_position": {0: torch.export.Dim.DYNAMIC}}
 
-    exported_program = torch.export.export_for_training(
+    exported_program = torch.export.export(
         model,
         args=(
             example_input_ids,
diff --git a/examples/llm_compression/torch_fx/tiny_llama/requirements.txt b/examples/llm_compression/torch_fx/tiny_llama/requirements.txt
index 973dafc1c83..1171c19f5ff 100644
--- a/examples/llm_compression/torch_fx/tiny_llama/requirements.txt
+++ b/examples/llm_compression/torch_fx/tiny_llama/requirements.txt
@@ -2,6 +2,6 @@ transformers==5.0.0
 datasets==5.0.0
 openvino==2026.2.1
 optimum==2.2.0
-torch==2.10.0
-torchvision==0.25.0
+torch==2.11.0
+torchvision==0.26.0
 torchao==0.17.0
diff --git a/examples/post_training_quantization/onnx/mobilenet_v2/requirements.txt b/examples/post_training_quantization/onnx/mobilenet_v2/requirements.txt
index 9dac86a4bbf..9443b033cf2 100644
--- a/examples/post_training_quantization/onnx/mobilenet_v2/requirements.txt
+++ b/examples/post_training_quantization/onnx/mobilenet_v2/requirements.txt
@@ -1,5 +1,5 @@
-torch==2.10.0
-torchvision==0.25.0
+torch==2.11.0
+torchvision==0.26.0
 fastdownload==0.0.7
 fastprogress==1.0.5
 fastcore==1.11.5
diff --git a/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/requirements.txt b/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/requirements.txt
index 0a2e51dd2aa..d61e2ef1301 100644
--- a/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/requirements.txt
+++ b/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/requirements.txt
@@ -3,4 +3,4 @@ onnx==1.22.0
 onnxruntime==1.23.2; python_version < '3.11'
 onnxruntime==1.24.3; python_version >= '3.11'
 openvino==2026.2.1
-torch==2.10.0
+torch==2.11.0
diff --git a/examples/post_training_quantization/openvino/yolo26/requirements.txt b/examples/post_training_quantization/openvino/yolo26/requirements.txt
index 6db9e7d0f5d..9e21c3078bc 100644
--- a/examples/post_training_quantization/openvino/yolo26/requirements.txt
+++ b/examples/post_training_quantization/openvino/yolo26/requirements.txt
@@ -1,4 +1,4 @@
 ultralytics==8.4.21
 onnx==1.22.0
 openvino==2026.2.1
-torch==2.10.0
+torch==2.11.0
diff --git a/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/requirements.txt b/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/requirements.txt
index 80be7d7c5bd..34ae08537b3 100644
--- a/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/requirements.txt
+++ b/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/requirements.txt
@@ -1,4 +1,4 @@
 ultralytics==8.3.221
 onnx==1.22.0
 openvino==2026.2.1
-torch==2.10.0
+torch==2.11.0
diff --git a/examples/post_training_quantization/torch/mobilenet_v2/requirements.txt b/examples/post_training_quantization/torch/mobilenet_v2/requirements.txt
index e8a2959d2e8..2c9a4422f27 100644
--- a/examples/post_training_quantization/torch/mobilenet_v2/requirements.txt
+++ b/examples/post_training_quantization/torch/mobilenet_v2/requirements.txt
@@ -3,5 +3,5 @@ fastprogress==1.0.5
 fastcore==1.11.5
 openvino==2026.2.1
 scikit-learn
-torch==2.10.0
-torchvision==0.25.0
+torch==2.11.0
+torchvision==0.26.0
diff --git a/examples/post_training_quantization/torch/ssd300_vgg16/requirements.txt b/examples/post_training_quantization/torch/ssd300_vgg16/requirements.txt
index 5a9dcfce168..c888ebd914c 100644
--- a/examples/post_training_quantization/torch/ssd300_vgg16/requirements.txt
+++ b/examples/post_training_quantization/torch/ssd300_vgg16/requirements.txt
@@ -4,7 +4,7 @@ fastcore==1.11.5
 onnx==1.22.0
 openvino==2026.2.1
 pycocotools==2.0.7
-torch==2.10.0
+torch==2.11.0
 torchmetrics==1.0.1
-torchvision==0.25.0
+torchvision==0.26.0
 numpy<2
diff --git a/examples/post_training_quantization/torch_fx/resnet18/requirements.txt b/examples/post_training_quantization/torch_fx/resnet18/requirements.txt
index e80334f4ce8..9ce280028e6 100644
--- a/examples/post_training_quantization/torch_fx/resnet18/requirements.txt
+++ b/examples/post_training_quantization/torch_fx/resnet18/requirements.txt
@@ -2,6 +2,6 @@ fastdownload==0.0.7
 fastprogress==1.0.5
 fastcore==1.11.5
 openvino==2026.2.1
-torch==2.10.0
-torchvision==0.25.0
+torch==2.11.0
+torchvision==0.26.0
 torchao==0.17.0
diff --git a/examples/pruning/torch/resnet18/requirements.txt b/examples/pruning/torch/resnet18/requirements.txt
index 891f3000246..adfc542602b 100644
--- a/examples/pruning/torch/resnet18/requirements.txt
+++ b/examples/pruning/torch/resnet18/requirements.txt
@@ -2,5 +2,5 @@ fastdownload==0.0.7
 fastprogress==1.0.5
 fastcore==1.11.5
 openvino==2026.2.1
-torch==2.10.0
-torchvision==0.25.0
+torch==2.11.0
+torchvision==0.26.0
diff --git a/examples/quantization_aware_training/torch/anomalib/requirements.txt b/examples/quantization_aware_training/torch/anomalib/requirements.txt
index 190f37056ba..8ec559dc405 100644
--- a/examples/quantization_aware_training/torch/anomalib/requirements.txt
+++ b/examples/quantization_aware_training/torch/anomalib/requirements.txt
@@ -1,5 +1,5 @@
 anomalib==2.2.0
-torch==2.10.0
+torch==2.11.0
 openvino==2025.3.0
 requests==2.33.0
 matplotlib==3.10.7
diff --git a/examples/quantization_aware_training/torch/resnet18/requirements.txt b/examples/quantization_aware_training/torch/resnet18/requirements.txt
index 891f3000246..adfc542602b 100644
--- a/examples/quantization_aware_training/torch/resnet18/requirements.txt
+++ b/examples/quantization_aware_training/torch/resnet18/requirements.txt
@@ -2,5 +2,5 @@ fastdownload==0.0.7
 fastprogress==1.0.5
 fastcore==1.11.5
 openvino==2026.2.1
-torch==2.10.0
-torchvision==0.25.0
+torch==2.11.0
+torchvision==0.26.0
diff --git a/src/nncf/torch/function_hook/handle_inner_functions.py b/src/nncf/torch/function_hook/handle_inner_functions.py
index 33541ccc960..fe38d73a1d9 100644
--- a/src/nncf/torch/function_hook/handle_inner_functions.py
+++ b/src/nncf/torch/function_hook/handle_inner_functions.py
@@ -21,6 +21,7 @@
 
 import torch
 from torch.nn.functional import _canonical_mask
+from torch.nn.functional import _check_key_padding_mask  # type:ignore[attr-defined]
 from torch.nn.functional import _in_projection  # type:ignore[attr-defined]
 from torch.nn.functional import _in_projection_packed  # type:ignore[attr-defined]
 from torch.nn.functional import _mha_shape_check  # type:ignore[attr-defined]
@@ -62,8 +63,10 @@ def multi_head_attention_forward(
     average_attn_weights: bool = True,
     is_causal: bool = False,
 ) -> Tuple[Tensor, Optional[Tensor]]:
+    is_batched = _mha_shape_check(
+        query, key, value, key_padding_mask, attn_mask, num_heads
+    )
 
-    is_batched = _mha_shape_check(query, key, value, key_padding_mask, attn_mask, num_heads)
     if not is_batched:
         query = query.unsqueeze(1)
         key = key.unsqueeze(1)
@@ -104,28 +107,48 @@ def multi_head_attention_forward(
         if key_padding_mask is not None:
             is_causal = False
 
-    assert (
-        embed_dim == embed_dim_to_check
-    ), f"was expecting embedding dimension of {embed_dim_to_check}, but got {embed_dim}"
+    if embed_dim != embed_dim_to_check:
+        raise AssertionError(
+            f"was expecting embedding dimension of {embed_dim_to_check}, but got {embed_dim}"
+        )
     if isinstance(embed_dim, torch.Tensor):
         head_dim = embed_dim.div(num_heads, rounding_mode="trunc")
     else:
         head_dim = embed_dim // num_heads
-    assert head_dim * num_heads == embed_dim, f"embed_dim {embed_dim} not divisible by num_heads {num_heads}"
+    if head_dim * num_heads != embed_dim:
+        raise AssertionError(
+            f"embed_dim {embed_dim} not divisible by num_heads {num_heads}"
+        )
     if use_separate_proj_weight:
-        assert (
-            key.shape[:2] == value.shape[:2]
-        ), f"key's sequence and batch dims {key.shape[:2]} do not match value's {value.shape[:2]}"
+        if key.shape[:2] != value.shape[:2]:
+            raise AssertionError(
+                f"key's sequence and batch dims {key.shape[:2]} do not match value's {value.shape[:2]}"
+            )
     else:
-        assert key.shape == value.shape, f"key shape {key.shape} does not match value shape {value.shape}"
+        if key.shape != value.shape:
+            raise AssertionError(
+                f"key shape {key.shape} does not match value shape {value.shape}"
+            )
 
     if not use_separate_proj_weight:
-        assert in_proj_weight is not None, "use_separate_proj_weight is False but in_proj_weight is None"
+        if in_proj_weight is None:
+            raise AssertionError(
+                "use_separate_proj_weight is False but in_proj_weight is None"
+            )
         q, k, v = _in_projection_packed(query, key, value, in_proj_weight, in_proj_bias)
     else:
-        assert q_proj_weight is not None, "use_separate_proj_weight is True but q_proj_weight is None"
-        assert k_proj_weight is not None, "use_separate_proj_weight is True but k_proj_weight is None"
-        assert v_proj_weight is not None, "use_separate_proj_weight is True but v_proj_weight is None"
+        if q_proj_weight is None:
+            raise AssertionError(
+                "use_separate_proj_weight is True but q_proj_weight is None"
+            )
+        if k_proj_weight is None:
+            raise AssertionError(
+                "use_separate_proj_weight is True but k_proj_weight is None"
+            )
+        if v_proj_weight is None:
+            raise AssertionError(
+                "use_separate_proj_weight is True but v_proj_weight is None"
+            )
         if in_proj_bias is None:
             b_q = b_k = b_v = None
         else:
@@ -157,11 +180,15 @@ def multi_head_attention_forward(
                     f"The shape of the 3D attn_mask is {attn_mask.shape}, but should be {correct_3d_size}."
                 )
         else:
-            raise RuntimeError(f"attn_mask's dimension {attn_mask.dim()} is not supported")
+            raise RuntimeError(
+                f"attn_mask's dimension {attn_mask.dim()} is not supported"
+            )
 
     if bias_k is not None and bias_v is not None:
-        assert static_k is None, "bias cannot be added to static key."
-        assert static_v is None, "bias cannot be added to static value."
+        if static_k is not None:
+            raise AssertionError("bias cannot be added to static key.")
+        if static_v is not None:
+            raise AssertionError("bias cannot be added to static value.")
         k = torch.cat([k, bias_k.repeat(1, bsz, 1)])
         v = torch.cat([v, bias_v.repeat(1, bsz, 1)])
         if attn_mask is not None:
@@ -169,31 +196,47 @@ def multi_head_attention_forward(
         if key_padding_mask is not None:
             key_padding_mask = pad(key_padding_mask, (0, 1))
     else:
-        assert bias_k is None
-        assert bias_v is None
+        if bias_k is not None:
+            raise AssertionError("bias_k is set but bias_v is None")
+        if bias_v is not None:
+            raise AssertionError("bias_v is set but bias_k is None")
 
     q = q.view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1)
     if static_k is None:
         k = k.view(k.shape[0], bsz * num_heads, head_dim).transpose(0, 1)
     else:
-        assert (
-            static_k.size(0) == bsz * num_heads
-        ), f"expecting static_k.size(0) of {bsz * num_heads}, but got {static_k.size(0)}"
-        assert static_k.size(2) == head_dim, f"expecting static_k.size(2) of {head_dim}, but got {static_k.size(2)}"
+        if static_k.size(0) != bsz * num_heads:
+            raise AssertionError(
+                f"expecting static_k.size(0) of {bsz * num_heads}, but got {static_k.size(0)}"
+            )
+        if static_k.size(2) != head_dim:
+            raise AssertionError(
+                f"expecting static_k.size(2) of {head_dim}, but got {static_k.size(2)}"
+            )
         k = static_k
     if static_v is None:
         v = v.view(v.shape[0], bsz * num_heads, head_dim).transpose(0, 1)
     else:
-        assert (
-            static_v.size(0) == bsz * num_heads
-        ), f"expecting static_v.size(0) of {bsz * num_heads}, but got {static_v.size(0)}"
-        assert static_v.size(2) == head_dim, f"expecting static_v.size(2) of {head_dim}, but got {static_v.size(2)}"
+        if static_v.size(0) != bsz * num_heads:
+            raise AssertionError(
+                f"expecting static_v.size(0) of {bsz * num_heads}, but got {static_v.size(0)}"
+            )
+        if static_v.size(2) != head_dim:
+            raise AssertionError(
+                f"expecting static_v.size(2) of {head_dim}, but got {static_v.size(2)}"
+            )
         v = static_v
 
     if add_zero_attn:
         zero_attn_shape = (bsz * num_heads, 1, head_dim)
-        k = torch.cat([k, torch.zeros(zero_attn_shape, dtype=k.dtype, device=k.device)], dim=1)
-        v = torch.cat([v, torch.zeros(zero_attn_shape, dtype=v.dtype, device=v.device)], dim=1)
+        k = torch.cat(
+            [k, torch.zeros(zero_attn_shape, dtype=k.dtype, device=k.device)],
+            dim=1,
+        )
+        v = torch.cat(
+            [v, torch.zeros(zero_attn_shape, dtype=v.dtype, device=v.device)],
+            dim=1,
+        )
         if attn_mask is not None:
             attn_mask = pad(attn_mask, (0, 1))
         if key_padding_mask is not None:
@@ -203,10 +246,12 @@ def multi_head_attention_forward(
 
     if key_padding_mask is not None:
         if not torch.jit.is_scripting() and not torch.jit.is_tracing():  # type: ignore
-            _check_key_padding_mask(key_padding_mask, src_len, bsz)  # type: ignore
+            _check_key_padding_mask(key_padding_mask, src_len, bsz)
 
         key_padding_mask = (
-            key_padding_mask.view(bsz, 1, 1, src_len).expand(-1, num_heads, -1, -1).reshape(bsz * num_heads, 1, src_len)
+            key_padding_mask.view(bsz, 1, 1, src_len)
+            .expand(-1, num_heads, -1, -1)
+            .reshape(bsz * num_heads, 1, src_len)
         )
         if attn_mask is None:
             attn_mask = key_padding_mask
@@ -217,13 +262,16 @@ def multi_head_attention_forward(
         dropout_p = 0.0
 
     if need_weights:
-        _B, _Nt, E = q.shape  # noqa: F841
+        _B, _Nt, E = q.shape
         q_scaled = q * math.sqrt(1.0 / float(E))
 
-        assert not (is_causal and attn_mask is None), "FIXME: is_causal not implemented for need_weights"
+        if is_causal and attn_mask is None:
+            raise AssertionError("FIXME: is_causal not implemented for need_weights")
 
         if attn_mask is not None:
-            attn_output_weights = torch.baddbmm(attn_mask, q_scaled, k.transpose(-2, -1))
+            attn_output_weights = torch.baddbmm(
+                attn_mask, q_scaled, k.transpose(-2, -1)
+            )
         else:
             attn_output_weights = torch.bmm(q_scaled, k.transpose(-2, -1))
         attn_output_weights = softmax(attn_output_weights, dim=-1)
@@ -232,7 +280,9 @@ def multi_head_attention_forward(
 
         attn_output = torch.bmm(attn_output_weights, v)
 
-        attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len * bsz, embed_dim)
+        attn_output = (
+            attn_output.transpose(0, 1).contiguous().view(tgt_len * bsz, embed_dim)
+        )
         attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
         attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1))
 
@@ -255,8 +305,12 @@ def multi_head_attention_forward(
         k = k.view(bsz, num_heads, src_len, head_dim)
         v = v.view(bsz, num_heads, src_len, head_dim)
 
-        attn_output = scaled_dot_product_attention(q, k, v, attn_mask, dropout_p, is_causal)
-        attn_output = attn_output.permute(2, 0, 1, 3).contiguous().view(bsz * tgt_len, embed_dim)
+        attn_output = scaled_dot_product_attention(
+            q, k, v, attn_mask, dropout_p, is_causal
+        )
+        attn_output = (
+            attn_output.permute(2, 0, 1, 3).contiguous().view(bsz * tgt_len, embed_dim)
+        )
 
         attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
         attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1))
diff --git a/tests/cross_fw/examples/example_scope.json b/tests/cross_fw/examples/example_scope.json
index 11857c49c16..a88d543ddca 100644
--- a/tests/cross_fw/examples/example_scope.json
+++ b/tests/cross_fw/examples/example_scope.json
@@ -355,14 +355,5 @@
         "accuracy_metrics": {
             "acc1": 54.96
         }
-    },
-    "llm_compression_torch_gptqmodel_convertor": {
-        "backend": "torch",
-        "device": "cuda",
-        "requirements": "examples/llm_compression/torch/gptq_model_convertor/requirements.txt",
-        "extra_requirements": "examples/llm_compression/torch/gptq_model_convertor/requirements_extra.txt",
-        "accuracy_metrics": {
-            "word_count": 28
-        }
     }
 }
diff --git a/tests/post_training/pipelines/fx_modelling.py b/tests/post_training/pipelines/fx_modelling.py
index 61b077d6761..0dc08a42aab 100644
--- a/tests/post_training/pipelines/fx_modelling.py
+++ b/tests/post_training/pipelines/fx_modelling.py
@@ -93,7 +93,7 @@ def convert_and_export_with_cache(model: PreTrainedModel):
 
     dynamic_shapes = {"input_ids": {1: torch.export.Dim.DYNAMIC}, "cache_position": {0: torch.export.Dim.DYNAMIC}}
 
-    exported_program = torch.export.export_for_training(
+    exported_program = torch.export.export(
         model,
         args=(
             example_input_ids,
diff --git a/tests/post_training/pipelines/image_classification_torchvision.py b/tests/post_training/pipelines/image_classification_torchvision.py
index 7fe8815edd4..f25d5eddaa7 100644
--- a/tests/post_training/pipelines/image_classification_torchvision.py
+++ b/tests/post_training/pipelines/image_classification_torchvision.py
@@ -24,8 +24,8 @@
 from tests.post_training.pipelines.image_classification_base import ImageClassificationBase
 
 
-def _torch_export_for_training(model: torch.nn.Module, args: tuple[Any, ...]) -> torch.fx.GraphModule:
-    return torch.export.export_for_training(model, args, strict=True).module(check_guards=False)
+def _torch_export(model: torch.nn.Module, args: tuple[Any, ...]) -> torch.fx.GraphModule:
+    return torch.export.export(model, args, strict=True).module(check_guards=False)
 
 
 def _torch_export(model: torch.nn.Module, args: tuple[Any, ...]) -> torch.fx.GraphModule:
@@ -43,13 +43,11 @@ class ImageClassificationTorchvision(ImageClassificationBase):
     """Pipeline for Image Classification model from torchvision repository"""
 
     models_vs_model_params = {
-        models.resnet18: VisionModelParams(models.ResNet18_Weights.DEFAULT, _torch_export_for_training),
-        models.mobilenet_v3_small: VisionModelParams(
-            models.MobileNet_V3_Small_Weights.DEFAULT, _torch_export_for_training
-        ),
+        models.resnet18: VisionModelParams(models.ResNet18_Weights.DEFAULT, _torch_export),
+        models.mobilenet_v3_small: VisionModelParams(models.MobileNet_V3_Small_Weights.DEFAULT, _torch_export),
         models.vit_b_16: VisionModelParams(
             models.ViT_B_16_Weights.DEFAULT,
-            _torch_export_for_training,
+            _torch_export,
             export_torch_before_ov_convert=False,  # OV convert of exported model has issues Issue-162009
         ),
         models.swin_v2_s: VisionModelParams(
diff --git a/tests/torch/conftest.py b/tests/torch/conftest.py
index 7af6044fbf9..286cc8bdd6a 100644
--- a/tests/torch/conftest.py
+++ b/tests/torch/conftest.py
@@ -22,13 +22,6 @@
 from nncf.common.quantization.structs import QuantizationScheme
 
 
-@pytest.fixture(scope="session", autouse=True)
-def disable_tf32_precision():
-    if torch:
-        torch.backends.cuda.matmul.fp32_precision = "ieee"
-        torch.backends.cudnn.conv.fp32_precision = "ieee"
-
-
 def pytest_addoption(parser: Parser):
     parser.addoption(
         "--regen-ref-data",
diff --git a/tests/torch/function_hook/test_wrapper.py b/tests/torch/function_hook/test_wrapper.py
index ac3d9b59f7a..833bb1f7b91 100644
--- a/tests/torch/function_hook/test_wrapper.py
+++ b/tests/torch/function_hook/test_wrapper.py
@@ -177,7 +177,7 @@ def test_compile_via_trace():
     torch.testing.assert_close(actual, return_origin + ADD_VALUE)
 
 
-@pytest.mark.parametrize("dynamo", [True, False])
+@pytest.mark.parametrize("dynamo", [True, False], ids=["dynamo", "no_dynamo"])
 def test_export_onnx(tmp_path: Path, dynamo: bool):
     example_input = helpers.ConvModel.get_example_inputs()
 
diff --git a/tests/torch/fx/helpers.py b/tests/torch/fx/helpers.py
index 24d451b8b9a..70a6bb51e61 100644
--- a/tests/torch/fx/helpers.py
+++ b/tests/torch/fx/helpers.py
@@ -54,13 +54,9 @@ def get_torch_fx_model(
         device_ex_input.append(inp.to(device))
     device_ex_input = tuple(device_ex_input)
 
-    # Temporary workaround for executorch tests. Can be removed once torch>=2.11.0
-    export_fn = (
-        torch.export.export_for_training if hasattr(torch.export, "export_for_training") else torch.export.export
-    )
     model.eval()
     with torch.no_grad():
-        return export_fn(model, args=device_ex_input, dynamic_shapes=dynamic_shapes, strict=True).module(
+        return torch.export.export(model, args=device_ex_input, dynamic_shapes=dynamic_shapes, strict=True).module(
             check_guards=False
         )
 
diff --git a/tests/torch/fx/test_model_transformer.py b/tests/torch/fx/test_model_transformer.py
index ab0095a5b6c..f0ce234bcf4 100644
--- a/tests/torch/fx/test_model_transformer.py
+++ b/tests/torch/fx/test_model_transformer.py
@@ -586,8 +586,6 @@ def test_constant_folding_scalar_clone(use_cuda):
 
     ex_input = torch.ones(model.INPUT_SIZE).cuda()
     with torch.no_grad():
-        # Use export function instead of export_for_training to
-        # reproduce SWIN model capturing
         captured_model = (
             torch.export.export(model, args=(ex_input,), strict=True)
             .run_decompositions(decomp_table={})
diff --git a/tests/torch/test_tensor.py b/tests/torch/test_tensor.py
index 39dff94e985..fa631f3eb4e 100644
--- a/tests/torch/test_tensor.py
+++ b/tests/torch/test_tensor.py
@@ -13,6 +13,7 @@
 
 from nncf.tensor import Tensor
 from nncf.tensor import TensorDataType
+from nncf.tensor import functions as fns
 from nncf.tensor.definitions import TensorBackend
 from nncf.tensor.definitions import TensorDeviceType
 from tests.cross_fw.test_templates.template_test_nncf_tensor import TemplateTestNNCFTensorOperators
@@ -76,3 +77,21 @@ def backend() -> TensorBackend:
     @staticmethod
     def device() -> TensorDeviceType:
         return TensorDeviceType.GPU
+
+    # TODO(AlexanderDokuchaev): Some tests cases failed on t4, ticket: 184285
+    @pytest.mark.parametrize(
+        "a, upper, ref",
+        (
+            ([[1.0, 0.0], [2.0, 1.0]], False, [[5.0, -2.0], [-2.0, 1.0]]),
+            ([[1.0, 2.0], [0.0, 1.0]], True, [[5.0, -2.0], [-2.0, 1.0]]),
+        ),
+    )
+    def test_fn_linalg_cholesky_inverse(self, a, upper, ref):
+        tensor_a = Tensor(self.to_tensor(a))
+        ref_tensor = self.to_tensor(ref)
+
+        res = fns.linalg.cholesky_inverse(tensor_a, upper=upper)
+
+        assert isinstance(res, Tensor)
+        assert fns.allclose(res.data, ref_tensor)
+        assert res.device == tensor_a.device