diff --git a/sycl/include/sycl/ext/oneapi/matrix/matrix-tensorcore.hpp b/sycl/include/sycl/ext/oneapi/matrix/matrix-tensorcore.hpp
index 5c6df9114b161..df3b6e97d258e 100644
--- a/sycl/include/sycl/ext/oneapi/matrix/matrix-tensorcore.hpp
+++ b/sycl/include/sycl/ext/oneapi/matrix/matrix-tensorcore.hpp
@@ -81,6 +81,10 @@ __SYCL_JOINT_MATRIX_OVERLOAD(uint8_t, a, 16, 16, int32_t, 2)
 __SYCL_JOINT_MATRIX_OVERLOAD(uint8_t, b, 16, 16, int32_t, 2)
 __SYCL_JOINT_MATRIX_OVERLOAD(int32_t, accumulator, 16, 16, int32_t, 8)
 
+// m16n16k8 tf32
+__SYCL_JOINT_MATRIX_OVERLOAD(uint32_t, a, 16, 8, int32_t, 4)
+__SYCL_JOINT_MATRIX_OVERLOAD(uint32_t, b, 8, 16, int32_t, 4)
+
 #undef __SYCL_JOINT_MATRIX_OVERLOAD
 } // namespace experimental::matrix
 
@@ -271,6 +275,15 @@ struct joint_matrix_load_impl<
         __dmma_m8n8k4_ld_c(res.data, src.get(), stride,
                            get_layout_id<Layout>());
       }
+    } else if constexpr (std::is_same<T, uint32_t>::value) {
+      int32_t *tileptr = reinterpret_cast<int32_t *>(src.get());
+      if constexpr (NumRows == 16 && NumCols == 8) {
+        __mma_tf32_m16n16k8_ld_a(res.data, tileptr, stride,
+                                 get_layout_id<Layout>());
+      } else if constexpr (NumRows == 8 && NumCols == 16) {
+        __mma_tf32_m16n16k8_ld_b(res.data, tileptr, stride,
+                                 get_layout_id<Layout>());
+      }
     }
   }
 };
@@ -495,6 +508,9 @@ struct joint_matrix_mad_impl<
                                      get_layout_pair_id<LayoutA, LayoutB>(), 0);
         }
       }
+    } else if constexpr (M == 16 && N == 16 && K == 8) {
+      __mma_tf32_m16n16k8_mma_f32(D.data, A.data, B.data, C.data,
+                                  get_layout_pair_id<LayoutA, LayoutB>(), 0);
     } else if constexpr (std::is_same<T1, double>::value) {
       __dmma_m8n8k4_mma_f64(D.data, A.data, B.data, C.data,
                             get_layout_pair_id<LayoutA, LayoutB>(), 0);
diff --git a/sycl/test/check_device_code/matrix/matrix-nvptx-tf32-test.cpp b/sycl/test/check_device_code/matrix/matrix-nvptx-tf32-test.cpp
new file mode 100644
index 0000000000000..709005a45b160
--- /dev/null
+++ b/sycl/test/check_device_code/matrix/matrix-nvptx-tf32-test.cpp
@@ -0,0 +1,119 @@
+// REQUIRES: cuda
+
+// RUN: %clangxx -fsycl-device-only -fsycl-targets=nvptx64-nvidia-cuda -Xsycl-target-backend --cuda-gpu-arch=sm_80 -DSYCL_EXT_ONEAPI_MATRIX=3 -S -Xclang -emit-llvm %s -o -| FileCheck %s
+
+// IMPORTANT: before updating sm version support beyond sm_86 read the following
+// NOTE!
+
+// NOTE: Technically the 'wrong' ptx instruction is called by
+// joint_matrix_load/joint_matrix_store in this case: notice that the load and
+// store instructions use shape m16n16k16, rather than the correct shape
+// m16n16k8. The 'wrong' ptx instruction is used because it returns the correct
+// SASS instructions for all existing supported sm versions: sm_80 and sm_86.
+// The reason for this ptx instruction redundancy is due to the ptx naming
+// convention for the mnk shape triple; however we cannot in principle a priori
+// know that future sm versions will behave in the same way and that this
+// redundancy will continue as future architecture is released. This should be
+// validated before supporting any sm versions beyond sm_86. The reason that we
+// choose to use the m16n16k16 instruction is that it allows the significant
+// advantage of being able to use a portable interface across Intel and Nvidia
+// backends.
+
+#include <CL/sycl.hpp>
+
+using namespace sycl;
+using namespace sycl::ext::oneapi::experimental::matrix;
+
+// M, N, K define the sizes of dimensions of the three matrix types (a, b,
+// accumulator) used per subgroup operation.
+constexpr int M = 16; // number of rows of accumulator,
+                      // number of cols of b.
+constexpr int N = 16; // number of cols of accumulator,
+                      // number of rows of a.
+constexpr int K = 8;  // number of cols of a/number of rows of b.
+
+// uint32_t is used in this test as the storage type for tf32
+uint32_t A[M * K];
+uint32_t B[K * N];
+float C[M * N];
+float D[M * N];
+
+int main() {
+
+  buffer<uint32_t, 1> bufA(A, range<1>(M * K));
+  buffer<uint32_t, 1> bufB(B, range<1>(K * N));
+  buffer<float, 1> bufC(C, range<1>(M * N));
+  buffer<float, 1> bufD(D, range<1>(M * N));
+
+  queue q;
+
+  q.submit([&](handler &cgh) {
+    auto accC = bufC.get_access<access::mode::read_write>(cgh);
+    auto accA = bufA.get_access<access::mode::read_write>(cgh);
+    auto accB = bufB.get_access<access::mode::read_write>(cgh);
+    auto accD = bufD.get_access<access::mode::read_write>(cgh);
+
+    cgh.parallel_for<class row_row>(
+        nd_range<2>({1, 32}, {1, 32}),
+        [=](nd_item<2> item) [[sycl::reqd_work_group_size(1, 1, 32)]] {
+          sycl::sub_group sg = item.get_sub_group();
+
+          joint_matrix<float, matrix_use::accumulator, M, N,
+                       matrix_layout::row_major>
+              sub_c;
+
+          joint_matrix<uint32_t, matrix_use::a, M, K, matrix_layout::row_major>
+              sub_a;
+
+          joint_matrix<uint32_t, matrix_use::b, K, N, matrix_layout::row_major>
+              sub_b;
+
+          //CHECK: tail call { float, float, float, float, float, float, float, float } @llvm.nvvm.wmma.m16n16k16.load.c.row.stride.f32.p1f32(float addrspace(1)* %_arg_, i32 16) #{{.*}}
+          joint_matrix_load(sg, sub_c, accC.get_pointer(), N);
+          //CHECK: tail call { i32, i32, i32, i32 } @llvm.nvvm.wmma.m16n16k8.load.a.row.stride.tf32.p0i32(i32* %call.ascast.i.i{{.*}}.i, i32 8) #{{.*}}
+          joint_matrix_load(sg, sub_a, accA.get_pointer(), K);
+          //CHECK: tail call { i32, i32, i32, i32 } @llvm.nvvm.wmma.m16n16k8.load.b.row.stride.tf32.p0i32(i32* %call.ascast.i.i{{.*}}.i, i32 16) #{{.*}}
+          joint_matrix_load(sg, sub_b, accB.get_pointer(), N);
+          //CHECK: tail call { float, float, float, float, float, float, float, float } @llvm.nvvm.wmma.m16n16k8.mma.row.row.tf32(i32 %10, i32 %11, i32 %12, i32 %13, i32 %15, i32 %16, i32 %17, i32 %18, float %1, float %2, float %3, float %4, float %5, float %6, float %7, float %8) #{{.*}}
+          sub_c = joint_matrix_mad(sg, sub_a, sub_b, sub_c);
+          //CHECK: tail call void @llvm.nvvm.wmma.m16n16k16.store.d.row.stride.f32.p1f32(float addrspace(1)* %_arg_14, float %20, float %21, float %22, float %23, float %24, float %25, float %26, float %27, i32 16) #{{.*}}
+          joint_matrix_store(sg, sub_c, accD.get_pointer(), N);
+        });
+  });
+
+  q.submit([&](handler &cgh) {
+    auto accC = bufC.get_access<access::mode::read_write>(cgh);
+    auto accA = bufA.get_access<access::mode::read_write>(cgh);
+    auto accB = bufB.get_access<access::mode::read_write>(cgh);
+    auto accD = bufD.get_access<access::mode::read_write>(cgh);
+
+    cgh.parallel_for<class col_col>(
+        nd_range<2>({1, 32}, {1, 32}),
+        [=](nd_item<2> item) [[sycl::reqd_work_group_size(1, 1, 32)]] {
+          sycl::sub_group sg = item.get_sub_group();
+
+          joint_matrix<float, matrix_use::accumulator, M, N,
+                       matrix_layout::col_major>
+              sub_c;
+
+          joint_matrix<uint32_t, matrix_use::a, M, K, matrix_layout::col_major>
+              sub_a;
+
+          joint_matrix<uint32_t, matrix_use::b, K, N, matrix_layout::col_major>
+              sub_b;
+
+          //CHECK: tail call { float, float, float, float, float, float, float, float } @llvm.nvvm.wmma.m16n16k16.load.c.col.stride.f32.p1f32(float addrspace(1)* %_arg_, i32 16) #{{.*}}
+          joint_matrix_load(sg, sub_c, accC.get_pointer(), N);
+          //CHECK: tail call { i32, i32, i32, i32 } @llvm.nvvm.wmma.m16n16k8.load.a.col.stride.tf32.p0i32(i32* %call.ascast.i.i{{.*}}.i, i32 8) #{{.*}}
+          joint_matrix_load(sg, sub_a, accA.get_pointer(), K);
+          //CHECK: tail call { i32, i32, i32, i32 } @llvm.nvvm.wmma.m16n16k8.load.b.col.stride.tf32.p0i32(i32* %call.ascast.i.i{{.*}}.i, i32 16) #{{.*}}
+          joint_matrix_load(sg, sub_b, accB.get_pointer(), N);
+          //CHECK: tail call { float, float, float, float, float, float, float, float } @llvm.nvvm.wmma.m16n16k8.mma.col.col.tf32(i32 %10, i32 %11, i32 %12, i32 %13, i32 %15, i32 %16, i32 %17, i32 %18, float %1, float %2, float %3, float %4, float %5, float %6, float %7, float %8) #{{.*}}
+          sub_c = joint_matrix_mad(sg, sub_a, sub_b, sub_c);
+          //CHECK: tail call void @llvm.nvvm.wmma.m16n16k16.store.d.col.stride.f32.p1f32(float addrspace(1)* %_arg_14, float %20, float %21, float %22, float %23, float %24, float %25, float %26, float %27, i32 16) #{{.*}}
+          joint_matrix_store(sg, sub_c, accD.get_pointer(), N);
+        });
+  });
+
+  return 0;
+};