diff --git a/Makefile b/Makefile
index 6e87a399a..a43f2a55a 100644
--- a/Makefile
+++ b/Makefile
@@ -41,6 +41,11 @@ fast_tests_diffusers:
 	python -m pip install .[tests]
 	python -m pytest tests/test_diffusers.py
 
+# Run single-card non-regression tests on image classification models
+fast_tests_image_classifications:
+	pip install timm
+	python -m pytest tests/test_image_classification.py
+
 # Run single-card non-regression tests
 slow_tests_1x: test_installs
 	python -m pytest tests/test_examples.py -v -s -k "single_card"
diff --git a/tests/test_image_classification.py b/tests/test_image_classification.py
new file mode 100644
index 000000000..6e59b7ac4
--- /dev/null
+++ b/tests/test_image_classification.py
@@ -0,0 +1,120 @@
+#
+# 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.
+
+import time
+from unittest import TestCase
+
+import habana_frameworks.torch as ht
+import numpy as np
+import requests
+import timm
+import torch
+from PIL import Image
+
+from optimum.habana.transformers.modeling_utils import adapt_transformers_to_gaudi
+
+
+adapt_transformers_to_gaudi()
+
+# For Gaudi 2
+LATENCY_FastViT_BF16_GRAPH_BASELINE = 2.5270626640319824
+
+
+class GaudiFastViTTester(TestCase):
+    """
+    Tests for FastViT model
+    """
+
+    def prepare_model_and_processor(self):
+        model = timm.create_model("timm/fastvit_t8.apple_in1k", pretrained=True)
+        model.to("hpu")
+        model = model.eval()
+        data_config = timm.data.resolve_model_data_config(model)
+        processor = timm.data.create_transform(**data_config, is_training=False)
+        return model, processor
+
+    def prepare_data(self):
+        url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png"
+        image = Image.open(requests.get(url, stream=True).raw)
+        return image
+
+    def test_inference_default(self):
+        model, processor = self.prepare_model_and_processor()
+        image = self.prepare_data()
+        inputs = processor(image).unsqueeze(0).to("hpu")
+        outputs = model(inputs)
+        top1_probabilities, top1_class_indices = torch.topk(outputs.softmax(dim=1) * 100, k=1)
+        top1_probabilities = top1_probabilities.to("cpu").detach().numpy()
+        top1_class_indices = top1_class_indices.to("cpu").numpy()
+        expected_scores = np.array([21.406523])  # from CPU
+        expected_class = np.array([960])
+        self.assertEqual(top1_class_indices, expected_class)
+        self.assertLess(np.abs(top1_probabilities - expected_scores).max(), 1)
+
+    def test_inference_autocast(self):
+        model, processor = self.prepare_model_and_processor()
+        image = self.prepare_data()
+        inputs = processor(image).unsqueeze(0).to("hpu")
+
+        with torch.autocast(device_type="hpu", dtype=torch.bfloat16):  # Autocast BF16
+            outputs = model(inputs)
+            top1_probabilities, top1_class_indices = torch.topk(outputs.softmax(dim=1) * 100, k=1)
+            top1_probabilities = top1_probabilities.to("cpu").detach().numpy()
+            top1_class_indices = top1_class_indices.to("cpu").numpy()
+            expected_scores = np.array([21.406523])  # from CPU
+            expected_class = np.array([960])
+            self.assertEqual(top1_class_indices, expected_class)
+            self.assertLess(np.abs(top1_probabilities - expected_scores).max(), 1)
+
+    def test_inference_hpu_graphs(self):
+        model, processor = self.prepare_model_and_processor()
+        image = self.prepare_data()
+        inputs = processor(image).unsqueeze(0).to("hpu")
+
+        model = ht.hpu.wrap_in_hpu_graph(model)  # Apply graph
+
+        outputs = model(inputs)
+        top1_probabilities, top1_class_indices = torch.topk(outputs.softmax(dim=1) * 100, k=1)
+        top1_probabilities = top1_probabilities.to("cpu").detach().numpy()
+        top1_class_indices = top1_class_indices.to("cpu").numpy()
+        expected_scores = np.array([21.406523])  # from CPU
+        expected_class = np.array([960])
+        self.assertEqual(top1_class_indices, expected_class)
+        self.assertLess(np.abs(top1_probabilities - expected_scores).max(), 1)
+
+    def test_no_latency_regression_autocast(self):
+        warmup = 3
+        iterations = 20
+
+        model, processor = self.prepare_model_and_processor()
+        image = self.prepare_data()
+
+        model = ht.hpu.wrap_in_hpu_graph(model)
+
+        with torch.no_grad(), torch.autocast(device_type="hpu", dtype=torch.bfloat16, enabled=True):
+            for i in range(warmup):
+                inputs = processor(image).unsqueeze(0).to("hpu")
+                _ = model(inputs)
+                torch.hpu.synchronize()
+
+            total_model_time = 0
+            for i in range(iterations):
+                inputs = processor(image).unsqueeze(0).to("hpu")
+                model_start_time = time.time()
+                _ = model(inputs)
+                torch.hpu.synchronize()
+                model_end_time = time.time()
+                total_model_time = total_model_time + (model_end_time - model_start_time)
+
+        latency = total_model_time * 1000 / iterations  # in terms of ms
+        self.assertLessEqual(latency, 1.05 * LATENCY_FastViT_BF16_GRAPH_BASELINE)