diff --git a/python/tvm/relay/op/contrib/cutlass.py b/python/tvm/relay/op/contrib/cutlass.py
index 4ae529e18dc2c..80c8ebffeb0f9 100644
--- a/python/tvm/relay/op/contrib/cutlass.py
+++ b/python/tvm/relay/op/contrib/cutlass.py
@@ -56,19 +56,51 @@ def make_batch_matmul_pattern():
 
 
 def make_conv2d_pattern():
-    # TODO(masahi): Check layout and alignment
     return is_op("nn.conv2d")(wildcard(), wildcard())
 
 
+def check_dtype(lhs, rhs):
+    """Check if dtypes in the given workload are supported by CUTLASS."""
+    return lhs.dtype == rhs.dtype and lhs.dtype == "float16" and rhs.dtype == "float16"
+
+
+def check_gemm(call):
+    """Check if the given dense workload can be offloaded to CUTLASS."""
+    lhs = call.args[0].checked_type
+    rhs = call.args[1].checked_type
+    return check_dtype(lhs, rhs)
+
+
+def check_batch_matmul(call):
+    """Check if the given batch_matmul workload can be offloaded to CUTLASS."""
+    transpose_a = call.attrs.transpose_a
+    transpose_b = call.attrs.transpose_b
+    return check_gemm(call) and transpose_a == False and transpose_b == True
+
+
+def check_conv2d(call):
+    """Check if the given conv2d workload can be offloaded to CUTLASS."""
+    data_layout = call.attrs.data_layout
+    kernel_layout = call.attrs.kernel_layout
+    data = call.args[0].checked_type
+    weight = call.args[1].checked_type
+    return data_layout == "NHWC" and kernel_layout == "OHWI" and check_dtype(data, weight)
+
+
 def partition_for_cutlass(mod):
     """Partition the input module into CUTLASS-supported subgraphs."""
-    dense_pat = ("cutlass.dense", make_gemm_pattern(False, None))
-    dense_bias_pat = ("cutlass.dense_bias", make_gemm_pattern(True, None))
-    dense_bias_relu_pat = ("cutlass.dense_bias_relu", make_gemm_pattern(True, "relu"))
-    dense_bias_gelu_fp16_pat = ("cutlass.dense_bias_gelu_fp16", make_gemm_pattern(True, "gelu"))
+    dense_pat = ("cutlass.dense", make_gemm_pattern(False, None), check_gemm)
+    dense_bias_pat = ("cutlass.dense_bias", make_gemm_pattern(True, None), check_gemm)
+    dense_bias_relu_pat = ("cutlass.dense_bias_relu", make_gemm_pattern(True, "relu"), check_gemm)
+    dense_bias_gelu_fp16_pat = (
+        "cutlass.dense_bias_gelu_fp16",
+        make_gemm_pattern(True, "gelu"),
+        check_gemm,
+    )
     dense_bias_gelu_fp32_pat = (
         "cutlass.dense_bias_gelu_fp32",
         make_gemm_pattern(True, "gelu", out_dtype="float32"),
+        check_gemm,
     )
     cutlass_patterns = [
         dense_bias_gelu_fp16_pat,
@@ -76,9 +108,9 @@ def partition_for_cutlass(mod):
         dense_bias_relu_pat,
         dense_bias_pat,
         dense_pat,
-        ("cutlass.batch_matmul", make_batch_matmul_pattern()),
+        ("cutlass.batch_matmul", make_batch_matmul_pattern(), check_batch_matmul),
         # TODO(masahi): Add more conv2d patterns
-        ("cutlass.conv2d", make_conv2d_pattern()),
+        ("cutlass.conv2d", make_conv2d_pattern(), check_conv2d),
     ]
     mod = transform.MergeComposite(cutlass_patterns)(mod)
     mod = transform.AnnotateTarget(["cutlass"])(mod)