diff --git a/.github/workflows/ttnn-run-sweeps.yaml b/.github/workflows/ttnn-run-sweeps.yaml
index 1dc1a5fc655e..d3b613d1dd1c 100644
--- a/.github/workflows/ttnn-run-sweeps.yaml
+++ b/.github/workflows/ttnn-run-sweeps.yaml
@@ -75,6 +75,7 @@ on:
           - eltwise.composite.binary.subalpha.subalpha
           - eltwise.composite.binary.minimum.minimum
           - eltwise.composite.binary.maximum.maximum
+          - eltwise.composite.binary.maximum.maximum_pytorch2
           - eltwise.ternary.addcmul.addcmul
           - eltwise.ternary.addcdiv.addcdiv
           - eltwise.ternary.mac.mac
diff --git a/tests/sweep_framework/sweeps/eltwise/composite/binary/maximum/maximum_pytorch2.py b/tests/sweep_framework/sweeps/eltwise/composite/binary/maximum/maximum_pytorch2.py
new file mode 100644
index 000000000000..978302999d21
--- /dev/null
+++ b/tests/sweep_framework/sweeps/eltwise/composite/binary/maximum/maximum_pytorch2.py
@@ -0,0 +1,95 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Optional, Tuple
+from functools import partial
+
+import torch
+import random
+import ttnn
+from tests.sweep_framework.utils import gen_shapes
+from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt
+
+from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
+from models.utility_functions import torch_random
+
+# Override the default timeout in seconds for hang detection.
+TIMEOUT = 30
+
+random.seed(0)
+
+
+# Parameters provided to the test vector generator are defined here.
+# They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
+# Each suite has a key name (in this case "suite_1" and "suite_2") which will associate the test vectors to this specific suite of inputs.
+# Developers can create their own generator functions and pass them to the parameters as inputs.
+parameters = {
+    "maximum_1": {
+        "input_shape": [
+            [1, 16, 1, 60],
+            # [1,16,s10+1],
+            [1, 16, 19, 19],
+            [1, 16, 59, 59],
+        ],
+        "input_a_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "input_b_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "input_a_layout": [ttnn.TILE_LAYOUT],
+        "input_b_layout": [ttnn.TILE_LAYOUT],
+        "input_a_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+        "input_b_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+        "output_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+    },
+}
+
+
+# This is the run instructions for the test, defined by the developer.
+# The run function must take the above-defined parameters as inputs.
+# The runner will call this run function with each test vector, and the returned results from this function will be stored.
+# If you defined a mesh_device_fixture above, the object you yielded will be passed into this function as 'device'. Otherwise, it will be the default ttnn device opened by the infra.
+def run(
+    input_shape,
+    input_a_dtype,
+    input_b_dtype,
+    input_a_layout,
+    input_b_layout,
+    input_a_memory_config,
+    input_b_memory_config,
+    output_memory_config,
+    *,
+    device,
+) -> list:
+    data_seed = random.randint(0, 20000000)
+    torch.manual_seed(data_seed)
+    torch_input_tensor_a = gen_func_with_cast_tt(
+        partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype
+    )(input_shape)
+
+    torch_input_tensor_b = gen_func_with_cast_tt(
+        partial(torch_random, low=-100, high=100, dtype=torch.float32), input_b_dtype
+    )(input_shape)
+
+    torch_output_tensor = torch.max(torch_input_tensor_a, torch_input_tensor_b)
+
+    input_tensor_a = ttnn.from_torch(
+        torch_input_tensor_a,
+        dtype=input_a_dtype,
+        layout=input_a_layout,
+        device=device,
+        memory_config=input_a_memory_config,
+    )
+
+    input_tensor_b = ttnn.from_torch(
+        torch_input_tensor_b,
+        dtype=input_b_dtype,
+        layout=input_b_layout,
+        device=device,
+        memory_config=input_b_memory_config,
+    )
+
+    start_time = start_measuring_time()
+    output_tensor = ttnn.maximum(input_tensor_a, input_tensor_b, memory_config=output_memory_config)
+    output_tensor = ttnn.to_torch(output_tensor)
+    e2e_perf = stop_measuring_time(start_time)
+
+    return [check_with_pcc(torch_output_tensor, output_tensor, 0.999), e2e_perf]