diff --git a/python/examples/einsum.py b/python/examples/einsum.py
index 4ec2dea36..d86edf847 100644
--- a/python/examples/einsum.py
+++ b/python/examples/einsum.py
@@ -13,7 +13,7 @@ configs = []
 MNK = [
         (512, 512 ,512), 
         (2048, 2048, 2048),
-        (8192, 8192, 8192),
+        #(8192, 8192, 8192),
        
         # (64, 64, 64000),
         # (64, 64, 128000),
@@ -68,7 +68,7 @@ NTHSE = [
         #  (128, 1024, 8, 64, 64), 
         #  (128, 1024, 8, 128, 128),
         #  (128, 1024, 8, 256, 256),
-          (128, 1024, 8, 256, 512)
+          #(128, 1024, 8, 256, 512)
         ]
 for N, T, H, S, E in NTHSE:
     configs += [([N, T, H, S], [H, E, S], [N, H, T, E], None, 'nths,hes->nhte', dict())]
@@ -92,10 +92,10 @@ for N, C, H, K, R in NCHKR:
 
 # 2D Dense convolution
 NCHWKRS = [
-          (8, 64, 128, 128, 768, 3, 3),
+          #(8, 64, 128, 128, 768, 3, 3),
           (8, 128, 64, 64, 256, 3, 3),
           (8, 256, 32, 32, 512, 3, 3),
-          (8, 512, 32, 32, 1024, 3, 3)
+          #(8, 512, 32, 32, 1024, 3, 3)
           ]
 for N, C, H, W, K, R, S in NCHWKRS:
     torch_fn = lambda a, b: torch.nn.functional.conv2d(a, b.permute(3, 0, 1, 2))
@@ -108,10 +108,10 @@ for N, C, H, W, K, R, S in NCHWKRS:
 
 # 3D Dense Convolution
 NCDHWKTRS = [
-           (8, 32, 27, 100, 100, 64, 3, 3, 3),
-           (8, 64, 23, 48, 48, 256, 3, 3, 3),
-           (8, 256, 19, 22, 22, 640, 3, 3, 3),
-           (8, 640, 15, 36, 36, 384, 3, 3, 3)
+           #(8, 32, 27, 100, 100, 64, 3, 3, 3),
+           #(8, 64, 23, 48, 48, 256, 3, 3, 3),
+           #(8, 256, 19, 22, 22, 640, 3, 3, 3),
+           #(8, 640, 15, 36, 36, 384, 3, 3, 3)
           ]
 for N, C, D, H, W, K, T, R, S in NCDHWKTRS:
     torch_fn = lambda a, b: torch.nn.functional.conv3d(a, b.permute(4, 0, 1, 2, 3))
diff --git a/python/triton/ops/einsum.py b/python/triton/ops/einsum.py
index 936a5fced..005ea8812 100644
--- a/python/triton/ops/einsum.py
+++ b/python/triton/ops/einsum.py
@@ -646,5 +646,4 @@ __global__ void {name}(
         return None, da, None, None, None
 
 
-
 einsum = _einsum.apply
\ No newline at end of file