diff --git a/python/tvm/contrib/cutlass/gen_gemm.py b/python/tvm/contrib/cutlass/gen_gemm.py
index cec64f0af974c..4132937874b57 100644
--- a/python/tvm/contrib/cutlass/gen_gemm.py
+++ b/python/tvm/contrib/cutlass/gen_gemm.py
@@ -141,12 +141,13 @@ def create_gemm_operator(
 # TODO(masahi): A sensible way to pick reasonable default kernels
 DEFAULT_KERNELS = {
     75: {
-        "float16": "cutlass_tensorop_h1688gemm_128x64_32x2_tn_align4",
-        "float32": "cutlass_tensorop_s1688gemm_f16_64x64_32x2_tn_align4",
+        "float16": "cutlass_tensorop_h1688gemm_128x64_32x2_tn_align1",
+        "float32": "cutlass_tensorop_s1688gemm_f16_64x64_32x2_tn_align1",
     },
+    # align1 variants do not seem to be available for sm80
     80: {
-        "float16": "cutlass_tensorop_h16816gemm_128x256_32x3_tn_align4",
-        "float32": "cutlass_tensorop_s16816gemm_f16_128x128_32x3_tn_align4",
+        "float16": "cutlass_tensorop_h1688gemm_128x64_32x2_tn_align1",
+        "float32": "cutlass_tensorop_s1688gemm_f16_64x64_32x2_tn_align1",
     },
 }
 
@@ -160,14 +161,16 @@ def __init__(self, sm, cutlass_path, binary_path):
         self.sm = sm
         self.cache = {}
 
-    def check_align(self, op_name, M):
+    def check_align(self, op_name, M, K):
         """Filter out kernels that cannot be supported."""
         aligns = re.findall(r"align[1|2|4|8]", op_name)
         assert len(aligns) == 1
+        # The same alignment is used for all axes
         align = int(aligns[0][-1])
-        if M % align != 0:
-            return False
-        return True
+        # TODO(masahi): CUTLASS alignment check on gemm kernels is too restrictive.
+        # See https://github.com/NVIDIA/cutlass/issues/362.
+        # When the above issue is resolved, we can remove the alignment check on M below.
+        return M % align == 0 and K % align == 0
 
     def get_default(self, out_dtype, batched=False):
         """Return the default kernel for the requested architecture.
@@ -194,7 +197,7 @@ def profile(
         ops = GENERATOR_FUNC_TABLE[self.sm](
             out_dtype, op_creator=partial(create_gemm_operator, batched=batched)
         )
-        ops = list(filter(lambda op: self.check_align(op["name"], M), ops))
+        ops = list(filter(lambda op: self.check_align(op["name"], M, K), ops))
 
         for op in ops:
             op["runtime"] = -1