diff --git a/tests/brevitas/nn/test_sdpa.py b/tests/brevitas/nn/test_sdpa.py
index 7e0de670d..9d735a997 100644
--- a/tests/brevitas/nn/test_sdpa.py
+++ b/tests/brevitas/nn/test_sdpa.py
@@ -28,7 +28,7 @@ class TestScaledDotProductAttention:
     @pytest.mark.parametrize("scale", [None, 0.3])
     @pytest.mark.parametrize("enable_gqa", [False, True])
     @pytest.mark.parametrize("rand_attn_mask", [False, True])
-    # Sanity check, since `ScaledDotProductAttention` just called `F.scaled_dot_product_attention` in its forward function
+    # Sanity check, since `ScaledDotProductAttention` just calls `F.scaled_dot_product_attention` in its forward function
     def test_sdpa_fwd(self, dropout_p, is_causal, scale, enable_gqa, rand_attn_mask):
         extra_kwargs = {
             "dropout_p": dropout_p,
@@ -56,3 +56,45 @@ def test_sdpa_fwd(self, dropout_p, is_causal, scale, enable_gqa, rand_attn_mask)
         out = m(q, k, v, attn_mask, **extra_kwargs)
         assert torch.isclose(out, ref_out, atol=ATOL).all()
         assert torch.isclose(out, ref_out, atol=ATOL).all()
+
+    @requires_pt_ge('2.0')
+    @pytest.mark.parametrize("dropout_p", [0.0, 0.5])
+    @pytest.mark.parametrize("is_causal", [True, False])
+    @pytest.mark.parametrize("scale", [None, 0.3])
+    @pytest.mark.parametrize("enable_gqa", [False, True])
+    @pytest.mark.parametrize("rand_attn_mask", [False, True])
+    def test_sdpa_quant_disabled_fwd(self, dropout_p, is_causal, scale, enable_gqa, rand_attn_mask):
+        extra_kwargs = {
+            "dropout_p": dropout_p,
+            "is_causal": is_causal,
+            "scale": scale,
+            "enable_gqa": enable_gqa,}
+        if torch_version < version.parse('2.5.0'):
+            del extra_kwargs["enable_gqa"]
+
+        kv_length = PAST_SEQUENCE_LENGTH + SEQUENCE_LENGTH
+        m = ScaledDotProductAttention()
+        qm = QuantScaledDotProductAttention(
+            softmax_input_quant=None,
+            attn_output_weights_quant=None,
+            q_scaled_quant=None,
+            k_transposed_quant=None,
+            v_quant=None,
+            attn_output_quant=None,
+        )
+        q = torch.randn(BATCH_SIZE, HEAD_DIM, SEQUENCE_LENGTH, EMBED_DIM)
+        k = torch.randn(BATCH_SIZE, HEAD_DIM, kv_length, EMBED_DIM)
+        v = torch.randn(BATCH_SIZE, HEAD_DIM, kv_length, EMBED_DIM)
+        if rand_attn_mask and not is_causal:
+            attn_mask = torch.randint(
+                low=0, high=2, size=(BATCH_SIZE, 1, SEQUENCE_LENGTH, kv_length), dtype=torch.bool)
+        else:
+            attn_mask = None
+        if dropout_p > 0.0:
+            torch.manual_seed(DROPOUT_SEED)
+        ref_out = m(q, k, v, attn_mask, **extra_kwargs)
+        if dropout_p > 0.0:
+            torch.manual_seed(DROPOUT_SEED)
+        out = qm(q, k, v, attn_mask, **extra_kwargs)
+        assert torch.isclose(out, ref_out, atol=ATOL).all()
+        assert torch.isclose(out, ref_out, atol=ATOL).all()