diff --git a/tests/ttnn/unit_tests/operations/eltwise/test_eltwise_typecast.py b/tests/ttnn/unit_tests/operations/eltwise/test_eltwise_typecast.py
index 287a30c79004..bc37a109a2e6 100644
--- a/tests/ttnn/unit_tests/operations/eltwise/test_eltwise_typecast.py
+++ b/tests/ttnn/unit_tests/operations/eltwise/test_eltwise_typecast.py
@@ -22,6 +22,12 @@
     ttnn.L1_MEMORY_CONFIG,
 ]
 
+TILE_HEIGHT = 32
+TILE_WIDTH = 32
+
+cpu_layout = ttnn.Layout.ROW_MAJOR
+npu_layout = ttnn.Layout.TILE
+
 
 @pytest.mark.parametrize(
     "pt_input_dtype, tt_input_dtype, tt_output_dtype",
@@ -98,3 +104,167 @@ def test_run_eltwise_typecast_op(
             device,
             test_args,
         )
+
+
+@skip_for_grayskull("Op not supported for Grayskull, supported for wormhole_b0")
+def test_typecast_bf16_to_bfp8_b(device):
+    torch.manual_seed(0)
+    shape = [32, 32]
+
+    # bf16 --> bfp8_b by cpu.
+    torch_bf16 = torch.randn(shape, dtype=torch.bfloat16)
+    bfp8_b_by_cpu = ttnn.Tensor(torch_bf16, ttnn.bfloat8_b).to(npu_layout)
+    cpu_version = bfp8_b_by_cpu.to(cpu_layout).to_torch()
+
+    # bf16 --> bfp8_b by npu
+    tt_bf16 = ttnn.Tensor(torch_bf16, ttnn.bfloat16).to(npu_layout).to(device)
+    bfp8_b_by_npu = ttnn.typecast(tt_bf16, ttnn.bfloat8_b)
+    npu_version = bfp8_b_by_npu.cpu().to(cpu_layout).to_torch()
+
+    passed = torch.equal(cpu_version, npu_version)
+    # print(cpu_version[0, 0:16])
+    # print(npu_version[0, 0:16])
+    assert passed
+
+
+def print_mismatches(cpu, npu, num_max_print):
+    different_indices = (cpu != npu).nonzero(as_tuple=True)
+    count = 0
+    for idx in zip(*different_indices):
+        count = count + 1
+        print(f"idx={idx} cpu={cpu[idx]} npu={npu[idx]}")
+        if count > num_max_print:
+            break
+
+
+@pytest.mark.parametrize("seed", [0, 2, 4, 6, 8])
+@pytest.mark.parametrize("scale", [1, 2, 4, 8, 16, 32, 64, 128, 256, 512])
+@pytest.mark.parametrize("bias", [0, 1, 2, 4, 8, 16, 32, 64, 128])
+@skip_for_grayskull("Op not supported for Grayskull, supported for wormhole_b0")
+def test_typecast_bf16_to_bfp8_b_various_input(seed, scale, bias, device):
+    torch.manual_seed(seed)
+    shape = [1024, 1024]
+
+    bias = bias
+    low = bias - scale
+    high = bias + scale
+    torch_bf16 = random_tensor = torch.empty(shape).uniform_(low, high).to(torch.bfloat16)
+
+    random_signs = torch.randint(0, 2, shape) * 2 - 1
+    torch_bf16 = torch_bf16 * random_signs
+
+    # bf16 --> bfp8_b by cpu.
+    bfp8_b_by_cpu = ttnn.Tensor(torch_bf16, ttnn.bfloat8_b).to(npu_layout)
+    cpu_version = bfp8_b_by_cpu.to(cpu_layout).to_torch()
+
+    # bf16 --> bfp8_b by npu
+    tt_bf16 = ttnn.Tensor(torch_bf16, ttnn.bfloat16).to(npu_layout).to(device)
+    bfp8_b_by_npu = ttnn.typecast(tt_bf16, ttnn.bfloat8_b)
+    npu_version = bfp8_b_by_npu.cpu().to(cpu_layout).to_torch()
+
+    passed = torch.equal(cpu_version, npu_version)
+    if not passed:
+        print_mismatches(cpu_version, npu_version, 16)
+    assert passed
+
+
+@pytest.mark.parametrize("seed", [0])
+@pytest.mark.parametrize("scale", [4])
+@pytest.mark.parametrize("bias", [2])
+# NaN becomes -Inf when converted to bfloat8_b format, skip testing
+@pytest.mark.parametrize("insert_inf, insert_nan", [[True, False]])  # , [False, True], [True, True]])
+@skip_for_grayskull("Op not supported for Grayskull, supported for wormhole_b0")
+def test_typecast_bf16_to_bfp8_b_with_inf_nan(seed, scale, bias, insert_inf, insert_nan, device):
+    torch.manual_seed(seed)
+    shape = [1024, 1024]
+
+    bias = bias
+    low = bias - scale
+    high = bias + scale
+
+    torch_bf16 = random_tensor = torch.empty(shape).uniform_(low, high).to(torch.bfloat16)
+    if insert_inf:
+        num_inf = torch_bf16.numel() // 8  # 16 elements are pcked into
+        inf_indices = torch.randint(0, torch_bf16.numel(), (num_inf,))
+        torch_bf16.view(-1)[inf_indices] = float("inf")
+    if insert_nan:
+        num_nan = torch_bf16.numel() // 8
+        nan_indices = torch.randint(0, torch_bf16.numel(), (num_nan,))
+        torch_bf16.view(-1)[nan_indices] = float("nan")
+    random_signs = torch.randint(0, 2, shape) * 2 - 1
+    torch_bf16 = torch_bf16 * random_signs
+
+    # bf16 --> bfp8_b by cpu.
+    bfp8_b_by_cpu = ttnn.Tensor(torch_bf16, ttnn.bfloat8_b).to(npu_layout)
+    cpu_version = bfp8_b_by_cpu.to(cpu_layout).to_torch()
+
+    # bf16 --> bfp8_b by npu
+    tt_bf16 = ttnn.Tensor(torch_bf16, ttnn.bfloat16).to(npu_layout).to(device)
+    bfp8_b_by_npu = ttnn.typecast(tt_bf16, ttnn.bfloat8_b)
+    npu_version = bfp8_b_by_npu.cpu().to(cpu_layout).to_torch()
+
+    passed = torch.equal(cpu_version, npu_version)
+    if not passed:
+        print_mismatches(cpu_version, npu_version, 16)
+    assert passed
+
+
+@skip_for_grayskull("Op not supported for Grayskull, supported for wormhole_b0")
+def test_typecast_bfp8_b_to_bf16(device):
+    torch.manual_seed(0)
+    shape = [1024, 1024]
+
+    # bfp8_b --> bf16 by cpu.
+    torch_bf16 = torch.randn(shape, dtype=torch.bfloat16)
+    bfp8_b = ttnn.Tensor(torch_bf16, ttnn.bfloat8_b).to(npu_layout)
+    cpu_version = bfp8_b.to(cpu_layout).to_torch()
+
+    # bfp8_b --> bf16 by npu.
+    bf16_by_npu = ttnn.typecast(bfp8_b.to(device), ttnn.bfloat16)
+    npu_version = bf16_by_npu.cpu().to(cpu_layout).to_torch()
+
+    passed = torch.equal(cpu_version, npu_version)
+    # print(cpu_version[0, 0:16])
+    # print(npu_version[0, 0:16])
+    assert passed
+
+
+@skip_for_grayskull("Op not supported for Grayskull, supported for wormhole_b0")
+def test_typecast_fp32_to_bfp8_b(device):
+    torch.manual_seed(0)
+    shape = [32, 32]
+
+    # fp32 --> bfp8_b by cpu.
+    torch_fp32 = torch.randn(shape, dtype=torch.float32)
+    bfp8_b_by_cpu = ttnn.Tensor(torch_fp32, ttnn.bfloat8_b).to(npu_layout)
+    cpu_version = bfp8_b_by_cpu.to(cpu_layout).to_torch()
+
+    # fp32 --> bfp8_b by npu
+    tt_fp32 = ttnn.Tensor(torch_fp32, ttnn.float32).to(npu_layout).to(device)
+    bfp8_b_by_npu = ttnn.typecast(tt_fp32, ttnn.bfloat8_b)
+    npu_version = bfp8_b_by_npu.cpu().to(cpu_layout).to_torch()
+
+    passed = torch.equal(cpu_version, npu_version)
+    # print(cpu_version[0, 0:16])
+    # print(npu_version[0, 0:16])
+    assert passed
+
+
+@skip_for_grayskull("Op not supported for Grayskull, supported for wormhole_b0")
+def test_typecast_bfp8_b_to_fp32(device):
+    torch.manual_seed(0)
+    shape = [1024, 1024]
+
+    # bfp8_b --> fp32 by cpu.
+    torch_fp32 = torch.randn(shape, dtype=torch.float32)
+    bfp8_b = ttnn.Tensor(torch_fp32, ttnn.bfloat8_b).to(npu_layout)
+    cpu_version = bfp8_b.to(cpu_layout).to_torch()
+
+    # bfp8_b --> fp32 by npu.
+    fp32_by_npu = ttnn.typecast(bfp8_b.to(device), ttnn.float32)
+    npu_version = fp32_by_npu.cpu().to(cpu_layout).to_torch()
+
+    passed = torch.equal(cpu_version, npu_version)
+    # print(cpu_version[0, 0:16])
+    # print(npu_version[0, 0:16])
+    assert passed
diff --git a/tests/ttnn/unit_tests/test_bfp8_bf16_conversion.py b/tests/ttnn/unit_tests/test_bfp8_bf16_conversion.py
deleted file mode 100644
index 7a4c600ec035..000000000000
--- a/tests/ttnn/unit_tests/test_bfp8_bf16_conversion.py
+++ /dev/null
@@ -1,168 +0,0 @@
-import pytest
-import torch
-
-import ttnn
-
-TILE_HEIGHT = 32
-TILE_WIDTH = 32
-
-cpu_layout = ttnn.Layout.ROW_MAJOR
-npu_layout = ttnn.Layout.TILE
-
-
-def test_typecast_bf16_to_bfp8_b(device):
-    torch.manual_seed(0)
-    shape = [32, 32]
-
-    # bf16 --> bfp8_b by cpu.
-    torch_bf16 = torch.randn(shape, dtype=torch.bfloat16)
-    bfp8_b_by_cpu = ttnn.Tensor(torch_bf16, ttnn.bfloat8_b).to(npu_layout)
-    cpu_version = bfp8_b_by_cpu.to(cpu_layout).to_torch()
-
-    # bf16 --> bfp8_b by npu
-    tt_bf16 = ttnn.Tensor(torch_bf16, ttnn.bfloat16).to(npu_layout).to(device)
-    bfp8_b_by_npu = ttnn.typecast(tt_bf16, ttnn.bfloat8_b)
-    npu_version = bfp8_b_by_npu.cpu().to(cpu_layout).to_torch()
-
-    passed = torch.equal(cpu_version, npu_version)
-    # print(cpu_version[0, 0:16])
-    # print(npu_version[0, 0:16])
-    assert passed
-
-
-def print_mismatches(cpu, npu, num_max_print):
-    different_indices = (cpu != npu).nonzero(as_tuple=True)
-    count = 0
-    for idx in zip(*different_indices):
-        count = count + 1
-        print(f"idx={idx} cpu={cpu[idx]} npu={npu[idx]}")
-        if count > num_max_print:
-            break
-
-
-@pytest.mark.parametrize("seed", [0, 2, 4, 6, 8])
-@pytest.mark.parametrize("scale", [1, 2, 4, 8, 16, 32, 64, 128, 256, 512])
-@pytest.mark.parametrize("bias", [0, 1, 2, 4, 8, 16, 32, 64, 128])
-def test_typecast_bf16_to_bfp8_b_various_input(seed, scale, bias, device):
-    torch.manual_seed(seed)
-    shape = [1024, 1024]
-
-    bias = bias
-    low = bias - scale
-    high = bias + scale
-    torch_bf16 = random_tensor = torch.empty(shape).uniform_(low, high).to(torch.bfloat16)
-
-    random_signs = torch.randint(0, 2, shape) * 2 - 1
-    torch_bf16 = torch_bf16 * random_signs
-
-    # bf16 --> bfp8_b by cpu.
-    bfp8_b_by_cpu = ttnn.Tensor(torch_bf16, ttnn.bfloat8_b).to(npu_layout)
-    cpu_version = bfp8_b_by_cpu.to(cpu_layout).to_torch()
-
-    # bf16 --> bfp8_b by npu
-    tt_bf16 = ttnn.Tensor(torch_bf16, ttnn.bfloat16).to(npu_layout).to(device)
-    bfp8_b_by_npu = ttnn.typecast(tt_bf16, ttnn.bfloat8_b)
-    npu_version = bfp8_b_by_npu.cpu().to(cpu_layout).to_torch()
-
-    passed = torch.equal(cpu_version, npu_version)
-    if not passed:
-        print_mismatches(cpu_version, npu_version, 16)
-    assert passed
-
-
-@pytest.mark.parametrize("seed", [0])
-@pytest.mark.parametrize("scale", [4])
-@pytest.mark.parametrize("bias", [2])
-# NaN becomes -Inf when converted to bfloat8_b format, skip testing
-@pytest.mark.parametrize("insert_inf, insert_nan", [[True, False]])  # , [False, True], [True, True]])
-def test_typecast_bf16_to_bfp8_b_with_inf_nan(seed, scale, bias, insert_inf, insert_nan, device):
-    torch.manual_seed(seed)
-    shape = [1024, 1024]
-
-    bias = bias
-    low = bias - scale
-    high = bias + scale
-
-    torch_bf16 = random_tensor = torch.empty(shape).uniform_(low, high).to(torch.bfloat16)
-    if insert_inf:
-        num_inf = torch_bf16.numel() // 8  # 16 elements are pcked into
-        inf_indices = torch.randint(0, torch_bf16.numel(), (num_inf,))
-        torch_bf16.view(-1)[inf_indices] = float("inf")
-    if insert_nan:
-        num_nan = torch_bf16.numel() // 8
-        nan_indices = torch.randint(0, torch_bf16.numel(), (num_nan,))
-        torch_bf16.view(-1)[nan_indices] = float("nan")
-    random_signs = torch.randint(0, 2, shape) * 2 - 1
-    torch_bf16 = torch_bf16 * random_signs
-
-    # bf16 --> bfp8_b by cpu.
-    bfp8_b_by_cpu = ttnn.Tensor(torch_bf16, ttnn.bfloat8_b).to(npu_layout)
-    cpu_version = bfp8_b_by_cpu.to(cpu_layout).to_torch()
-
-    # bf16 --> bfp8_b by npu
-    tt_bf16 = ttnn.Tensor(torch_bf16, ttnn.bfloat16).to(npu_layout).to(device)
-    bfp8_b_by_npu = ttnn.typecast(tt_bf16, ttnn.bfloat8_b)
-    npu_version = bfp8_b_by_npu.cpu().to(cpu_layout).to_torch()
-
-    passed = torch.equal(cpu_version, npu_version)
-    if not passed:
-        print_mismatches(cpu_version, npu_version, 16)
-    assert passed
-
-
-def test_typecast_bfp8_b_to_bf16(device):
-    torch.manual_seed(0)
-    shape = [1024, 1024]
-
-    # bfp8_b --> bf16 by cpu.
-    torch_bf16 = torch.randn(shape, dtype=torch.bfloat16)
-    bfp8_b = ttnn.Tensor(torch_bf16, ttnn.bfloat8_b).to(npu_layout)
-    cpu_version = bfp8_b.to(cpu_layout).to_torch()
-
-    # bfp8_b --> bf16 by npu.
-    bf16_by_npu = ttnn.typecast(bfp8_b.to(device), ttnn.bfloat16)
-    npu_version = bf16_by_npu.cpu().to(cpu_layout).to_torch()
-
-    passed = torch.equal(cpu_version, npu_version)
-    # print(cpu_version[0, 0:16])
-    # print(npu_version[0, 0:16])
-    assert passed
-
-
-def test_typecast_fp32_to_bfp8_b(device):
-    torch.manual_seed(0)
-    shape = [32, 32]
-
-    # fp32 --> bfp8_b by cpu.
-    torch_fp32 = torch.randn(shape, dtype=torch.float32)
-    bfp8_b_by_cpu = ttnn.Tensor(torch_fp32, ttnn.bfloat8_b).to(npu_layout)
-    cpu_version = bfp8_b_by_cpu.to(cpu_layout).to_torch()
-
-    # fp32 --> bfp8_b by npu
-    tt_fp32 = ttnn.Tensor(torch_fp32, ttnn.float32).to(npu_layout).to(device)
-    bfp8_b_by_npu = ttnn.typecast(tt_fp32, ttnn.bfloat8_b)
-    npu_version = bfp8_b_by_npu.cpu().to(cpu_layout).to_torch()
-
-    passed = torch.equal(cpu_version, npu_version)
-    # print(cpu_version[0, 0:16])
-    # print(npu_version[0, 0:16])
-    assert passed
-
-
-def test_typecast_bfp8_b_to_fp32(device):
-    torch.manual_seed(0)
-    shape = [1024, 1024]
-
-    # bfp8_b --> fp32 by cpu.
-    torch_fp32 = torch.randn(shape, dtype=torch.float32)
-    bfp8_b = ttnn.Tensor(torch_fp32, ttnn.bfloat8_b).to(npu_layout)
-    cpu_version = bfp8_b.to(cpu_layout).to_torch()
-
-    # bfp8_b --> fp32 by npu.
-    fp32_by_npu = ttnn.typecast(bfp8_b.to(device), ttnn.float32)
-    npu_version = fp32_by_npu.cpu().to(cpu_layout).to_torch()
-
-    passed = torch.equal(cpu_version, npu_version)
-    # print(cpu_version[0, 0:16])
-    # print(npu_version[0, 0:16])
-    assert passed