diff --git a/.github/workflows/conda.yml b/.github/workflows/conda.yml
index 32f970c268..2d7483998d 100644
--- a/.github/workflows/conda.yml
+++ b/.github/workflows/conda.yml
@@ -19,7 +19,7 @@ on:
 
 env:
   # you need at least cuda 5.0 for some of the stuff compiled here.
-  TORCH_CUDA_ARCH_LIST: "5.0+PTX 6.0 6.1 7.0 7.5 8.0+PTX"
+  TORCH_CUDA_ARCH_LIST: "6.0+PTX 6.1 7.0 7.5 8.0+PTX"
   MAX_JOBS: 3  # Avoids OOMs
   XFORMERS_BUILD_TYPE: "Release"
   XFORMERS_PACKAGE_FROM: "conda-${{ github.ref_name }}"
diff --git a/.github/workflows/wheels_build.yml b/.github/workflows/wheels_build.yml
index 0b398822b3..68ab3bf444 100644
--- a/.github/workflows/wheels_build.yml
+++ b/.github/workflows/wheels_build.yml
@@ -26,7 +26,7 @@ on:
 
 env:
   # you need at least cuda 5.0 for some of the stuff compiled here.
-  TORCH_CUDA_ARCH_LIST: "5.0+PTX 6.0 6.1 7.0 7.5 8.0+PTX"
+  TORCH_CUDA_ARCH_LIST: "6.0+PTX 6.1 7.0 7.5 8.0+PTX"
   MAX_JOBS: 4
   DISTUTILS_USE_SDK: 1 # otherwise distutils will complain on windows about multiple versions of msvc
   XFORMERS_BUILD_TYPE: "Release"
diff --git a/setup.py b/setup.py
index 327e1f7df6..c8b78dd1f7 100644
--- a/setup.py
+++ b/setup.py
@@ -316,7 +316,9 @@ def get_extensions():
     extra_compile_args = {"cxx": ["-O3", "-std=c++17"]}
     if sys.platform == "win32":
         define_macros += [("xformers_EXPORTS", None)]
-        extra_compile_args["cxx"].extend(["/MP", "/Zc:lambda", "/Zc:preprocessor"])
+        extra_compile_args["cxx"].extend(
+            ["/MP", "/Zc:lambda", "/Zc:preprocessor", "/Zc:__cplusplus"]
+        )
     elif "OpenMP not found" not in torch.__config__.parallel_info():
         extra_compile_args["cxx"].append("-fopenmp")
 
@@ -360,6 +362,8 @@ def get_extensions():
                 "/Zc:lambda",
                 "-Xcompiler",
                 "/Zc:preprocessor",
+                "-Xcompiler",
+                "/Zc:__cplusplus",
             ]
         extra_compile_args["nvcc"] = nvcc_flags
 
diff --git a/third_party/cutlass b/third_party/cutlass
index e0aaa3c3b3..7d49e6c7e2 160000
--- a/third_party/cutlass
+++ b/third_party/cutlass
@@ -1 +1 @@
-Subproject commit e0aaa3c3b38db9a89c31f04fef91e92123ad5e2e
+Subproject commit 7d49e6c7e2f8896c47f586706e67e1fb215529dc
diff --git a/xformers/csrc/attention/cuda/fmha/kernel_backward.h b/xformers/csrc/attention/cuda/fmha/kernel_backward.h
index 9ed0e6ba49..41eb985c01 100644
--- a/xformers/csrc/attention/cuda/fmha/kernel_backward.h
+++ b/xformers/csrc/attention/cuda/fmha/kernel_backward.h
@@ -1441,7 +1441,7 @@ struct AttentionBackwardKernel {
       uint8_t lane_id) {
     cutlass::Array<cutlass::uint1b_t, MatmulDOIVJ::Mma::FragmentC::kElements>
         dropout_keep_mask_doivj;
-    dropout_keep_mask_doivj.fill(1);
+    dropout_keep_mask_doivj.fill(cutlass::uint1b_t{1});
     const float dropout_scale =
         kApplyDropout ? 1.0 / (1.0 - p.dropout_prob) : 1.0f;
 
diff --git a/xformers/csrc/sparse24/compute_sparse_tile.h b/xformers/csrc/sparse24/compute_sparse_tile.h
index 224998ec41..464746321c 100644
--- a/xformers/csrc/sparse24/compute_sparse_tile.h
+++ b/xformers/csrc/sparse24/compute_sparse_tile.h
@@ -85,8 +85,8 @@ struct LargestValuesGreedy {
       for (int j = 0; j < 4; ++j) {
         TileValueOrdered& v = values_ordered[i * 4 + j];
         v.parts.value = values.at(i, j).get();
-        v.parts.col = j;
-        v.parts.row = i;
+        v.parts.col = uint2b_t{j};
+        v.parts.row = uint2b_t{i};
       }
     }
     // Use a sorting network (aka without branches) to avoid
@@ -150,7 +150,7 @@ struct Causal1122 {
       for (int col = 0; col < 4; ++col) {
         TileValueOrdered& v = values_ordered[col];
         v.parts.value = values.at(row, col).get();
-        v.parts.col = col;
+        v.parts.col = uint2b_t{col};
       }
       // Use a sorting network (aka without branches) to avoid
       // warp divergence
diff --git a/xformers/csrc/sparse24/sparse24_pack.h b/xformers/csrc/sparse24/sparse24_pack.h
index 2158966b97..7ca874d30e 100644
--- a/xformers/csrc/sparse24/sparse24_pack.h
+++ b/xformers/csrc/sparse24/sparse24_pack.h
@@ -77,9 +77,9 @@ warp_shuffle_meta(uint32_t meta_ab, bool transposed = false) {
       uint8b_t(meta_ab >> (8 * (thread_left + 2)))};
   // shfl t0-t4 / t1-t5
   stage0_data[0] =
-      __shfl_xor_sync(0xffffffff, stage0_data[0], transposed ? 1 : 4);
+      uint8b_t{__shfl_xor_sync(0xffffffff, stage0_data[0], transposed ? 1 : 4)};
   stage0_data[1] =
-      __shfl_xor_sync(0xffffffff, stage0_data[1], transposed ? 1 : 4);
+      uint8b_t{__shfl_xor_sync(0xffffffff, stage0_data[1], transposed ? 1 : 4)};
 
   uint16_t line0 = int(uint8b_t(meta_ab >> (8 * (1 - thread_left))))
       << ((1 - thread_left) * 8);
@@ -229,24 +229,24 @@ struct KernelTypes {
       // We know that col0 is always packed to position 0 if it's there
       // and col1 is packed to pos 0 or 1 (depending if col0 is selected)
       if (isSelected(1)) {
-        packValue(0, 1);
+        packValue(uint2b_t{0}, uint2b_t{1});
       }
       if (isSelected(0)) {
-        packValue(0, 0);
+        packValue(uint2b_t{0}, uint2b_t{0});
       }
       if (isSelected(0) && isSelected(1)) {
-        packValue(1, 1);
+        packValue(uint2b_t{1}, uint2b_t{1});
       }
       // Process cols 2/3
       // same sort of heuristic
       if (isSelected(2)) {
-        packValue(1, 2);
+        packValue(uint2b_t{1}, uint2b_t{2});
       }
       if (isSelected(3)) {
-        packValue(1, 3);
+        packValue(uint2b_t{1}, uint2b_t{3});
       }
       if (isSelected(2) && isSelected(3)) {
-        packValue(0, 2);
+        packValue(uint2b_t{0}, uint2b_t{2});
       }
       int add_mask = (col0_from | (col1_from << 2)) << (8 * row + meta_pos);
       meta |= add_mask;