diff --git a/example/01_gemm/gemm_dl_int4.cpp b/example/01_gemm/gemm_dl_int4.cpp
index 8d1ed01284..9714c33dad 100644
--- a/example/01_gemm/gemm_dl_int4.cpp
+++ b/example/01_gemm/gemm_dl_int4.cpp
@@ -20,6 +20,7 @@
 #include "ck/library/utility/device_memory.hpp"
 #include "ck/library/utility/host_tensor.hpp"
 #include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
 #include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
 
 template <ck::index_t... Is>
@@ -62,6 +63,8 @@ using ReferenceGemmInstance = ck::tensor_operation::host::
 
 int main(int argc, char* argv[])
 {
+    using namespace ck::literals;
+
     bool do_verification = true;
     int init_method      = 1;
     bool time_kernel     = false;
@@ -111,15 +114,13 @@ int main(int argc, char* argv[])
 
     auto f_host_tensor_descriptor =
         [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
-            if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
+            if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
             {
-                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
-                                            std::vector<std::size_t>({stride, 1}));
+                return HostTensorDescriptor({row, col}, {stride, 1_uz});
             }
             else
             {
-                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
-                                            std::vector<std::size_t>({1, stride}));
+                return HostTensorDescriptor({row, col}, {1_uz, stride});
             }
         };
 
@@ -189,7 +190,7 @@ int main(int argc, char* argv[])
 
     float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
 
-    std::size_t flop      = std::size_t(2) * M * N * K;
+    std::size_t flop      = 2_uz * M * N * K;
     std::size_t num_btype = sizeof(KernelADataType) * M * K + sizeof(KernelBDataType) * K * N +
                             sizeof(KernelCDataType) * M * N;
 
diff --git a/example/01_gemm/gemm_xdl_int4.cpp b/example/01_gemm/gemm_xdl_int4.cpp
index 95274da196..9b97a44fba 100644
--- a/example/01_gemm/gemm_xdl_int4.cpp
+++ b/example/01_gemm/gemm_xdl_int4.cpp
@@ -21,6 +21,7 @@
 #include "ck/library/utility/device_memory.hpp"
 #include "ck/library/utility/host_tensor.hpp"
 #include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
 #include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
 
 template <ck::index_t... Is>
@@ -64,6 +65,8 @@ using ReferenceGemmInstance = ck::tensor_operation::host::
 
 int main(int argc, char* argv[])
 {
+    using namespace ck::literals;
+
     bool do_verification = true;
     int init_method      = 1;
     bool time_kernel     = false;
@@ -109,15 +112,13 @@ int main(int argc, char* argv[])
 
     auto f_host_tensor_descriptor =
         [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
-            if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
+            if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
             {
-                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
-                                            std::vector<std::size_t>({stride, 1}));
+                return HostTensorDescriptor({row, col}, {stride, 1_uz});
             }
             else
             {
-                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
-                                            std::vector<std::size_t>({1, stride}));
+                return HostTensorDescriptor({row, col}, {1_uz, stride});
             }
         };
 
@@ -183,7 +184,7 @@ int main(int argc, char* argv[])
 
     float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
 
-    std::size_t flop      = std::size_t(2) * M * N * K;
+    std::size_t flop      = 2_uz * M * N * K;
     std::size_t num_btype = sizeof(KernelADataType) * M * K + sizeof(KernelBDataType) * K * N +
                             sizeof(KernelCDataType) * M * N;