diff --git a/CMakeLists.txt b/CMakeLists.txt
index a61da8afa33..13b474b1ee6 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -95,6 +95,7 @@ list(APPEND NVFUSER_SRCS
   ${NVFUSER_SRCS_DIR}/device_lower/analysis/index_compute.cpp
   ${NVFUSER_SRCS_DIR}/device_lower/analysis/predicate_elimination.cpp
   ${NVFUSER_SRCS_DIR}/device_lower/analysis/sync_information.cpp
+  ${NVFUSER_SRCS_DIR}/device_lower/analysis/tensor_memory.cpp
   ${NVFUSER_SRCS_DIR}/device_lower/analysis/thread_predicate.cpp
   ${NVFUSER_SRCS_DIR}/device_lower/analysis/tma.cpp
   ${NVFUSER_SRCS_DIR}/device_lower/analysis/trivial_broadcast.cpp
diff --git a/csrc/codegen.cpp b/csrc/codegen.cpp
index d9a5ba6bdcd..3b5ca0847b0 100644
--- a/csrc/codegen.cpp
+++ b/csrc/codegen.cpp
@@ -683,6 +683,11 @@ class CudaKernelGenerator : private kir::ConstIrVisitor {
       return;
     }
 
+    if (ti->view()->getMemoryType() == MemoryType::Tensor) {
+      code_ << genInline(ti->index());
+      return;
+    }
+
     if (ti->view()->getMemoryType() == MemoryType::Global &&
         kernel_->summary().sync_map->needsRawSync(ti->view()).hasBID()) {
       code_ << "*(volatile " << ti->getDataType().value() << "*)&";
@@ -3186,7 +3191,7 @@ class CudaKernelGenerator : private kir::ConstIrVisitor {
           break;
         }
         case MemoryType::Tensor: {
-          NVF_THROW("Not implemented yet");
+          // Do nothing for now. This behavior will change soon.
           break;
         }
         default:
diff --git a/csrc/device_lower/analysis/tensor_memory.cpp b/csrc/device_lower/analysis/tensor_memory.cpp
new file mode 100644
index 00000000000..2b52fd15bbd
--- /dev/null
+++ b/csrc/device_lower/analysis/tensor_memory.cpp
@@ -0,0 +1,26 @@
+// clang-format off
+/*
+ * SPDX-FileCopyrightText: Copyright (c) 2023-present NVIDIA CORPORATION & AFFILIATES.
+ * All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ */
+// clang-format on
+
+#include <device_lower/analysis/tensor_memory.h>
+#include <fusion.h>
+#include <ir/all_nodes.h>
+
+namespace nvfuser {
+
+TensorMemoryInfo computeTMemInfo(Fusion* fusion) {
+  bool found = false;
+  for (auto tv : fusion->allTvs()) {
+    if (tv->getMemoryType() == MemoryType::Tensor) {
+      NVF_ERROR(!found, "Only one tensor on TMem is supported");
+      found = true;
+    }
+  }
+  return {};
+}
+
+} // namespace nvfuser
diff --git a/csrc/device_lower/analysis/tensor_memory.h b/csrc/device_lower/analysis/tensor_memory.h
new file mode 100644
index 00000000000..9038e171839
--- /dev/null
+++ b/csrc/device_lower/analysis/tensor_memory.h
@@ -0,0 +1,65 @@
+// clang-format off
+/*
+ * SPDX-FileCopyrightText: Copyright (c) 2023-present NVIDIA CORPORATION & AFFILIATES.
+ * All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ */
+// clang-format on
+#pragma once
+
+namespace nvfuser {
+
+class Fusion;
+
+// Information used to lower tensor memory. So far, there is no information
+// needed, the computeTMemInfo just check that there is only one tensor on TMem
+// in the fusion. This limitation is described in the note below, and it is only
+// for incremental development. This limitation will be removed soon in the
+// future.
+struct TensorMemoryInfo;
+TensorMemoryInfo computeTMemInfo(Fusion* fusion);
+
+// Note: [Tensor Memory Allocation]
+//
+// Tensor memory is a very special memory, so its allocation is also very
+// different from other memory types.
+//
+// It is highly recommended to read the PTX documentation for tensor memory
+// if you are not alreay familiar with it:
+// https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#tensor-memory
+//
+// The first thing to note is, TMem does not have virtualization. This means:
+// We can not just allocate starting from address 0 like how we allocate shared
+// memory, and rely on page table to translate the same virtual address of
+// different CTA to different physical address. There is no virtual TMem
+// address. All addresses are physical addresses.
+//
+// Because multiple CTAs can execute on the same SM simultaneously, there must
+// be some handshaking mechanism for each CTA to know the region of TMem that it
+// can use. This is done by using the PTX instruction tcgen05.alloc. To ensure
+// safety, there is a mutex "I have the right to allocate TMem" in the
+// hardware. At the beginning of each CTA, the CTA will try to acquire the mutex
+// automatically. If it fails, the CTA will be blocked until the mutex is free.
+// This means, only one CTA can allocate TMem at a time. Once the CTA has
+// finished allocating TMem, it should release the mutex to relinquish the right
+// to allocate. After the right to allocate is relinquished, this CTA can not
+// allocate new TMem any more, but it can still access the TMem that it has
+// allocated, and it can also free the TMem that it has allocated. Once one CTA
+// relinquishes the right to allocate, the next CTA that is blocked will be
+// unblocked and can acquire the mutex to allocate TMem.
+//
+// Currently, the TMem allocation is not supported in nvFuser. We currently only
+// allow one TensorView to be on TMem, and because we never relinquish the right
+// to allocate TMem, CTA will be serialized on SM. A new CTA can be scheduled on
+// an SM only after the previous CTA on that SM has completely finished
+// executing. Thanks to this serialization, we can just skip allocating and
+// think that our only TMem TensorView own the entire TMem, because we are sure
+// that there will not be another CTA using that address. As a result, we could
+// just provide address 0 to our instructions that access TMem. In principle, it
+// is clearly wrong to write to an address that is not allocated, but because we
+// are sure that it will in practice work for the specific unit test that we are
+// targeting, we just do it so we have incremental development.
+
+struct TensorMemoryInfo {};
+
+} // namespace nvfuser
diff --git a/csrc/device_lower/lower2device.cpp b/csrc/device_lower/lower2device.cpp
index 51107107192..edff33d8e68 100644
--- a/csrc/device_lower/lower2device.cpp
+++ b/csrc/device_lower/lower2device.cpp
@@ -599,6 +599,9 @@ void GpuLower::analysis(Fusion* fusion) {
 
   consumerToTMAInfo() = getConsumerToTMAInfoMap(fusion_);
   dumpExprsIfEnabled(fusion_->exprs(), "getConsumerToTMAInfoMap");
+
+  tmemInfo() = computeTMemInfo(fusion_);
+  dumpExprsIfEnabled(fusion_->exprs(), "computeTMemInfo");
 }
 
 kir::Kernel* GpuLower::kernel() const {
diff --git a/csrc/device_lower/lower2device.h b/csrc/device_lower/lower2device.h
index 7e35384481b..4d101824fad 100644
--- a/csrc/device_lower/lower2device.h
+++ b/csrc/device_lower/lower2device.h
@@ -14,6 +14,7 @@
 #include <device_lower/analysis/fused_reduction.h>
 #include <device_lower/analysis/predicate_elimination.h>
 #include <device_lower/analysis/sync_information.h>
+#include <device_lower/analysis/tensor_memory.h>
 #include <device_lower/analysis/thread_predicate.h>
 #include <device_lower/analysis/tma.h>
 #include <device_lower/analysis/trivial_broadcast.h>
@@ -268,6 +269,14 @@ class GpuLower : public NonCopyable {
     return consumer_to_tma_info_;
   }
 
+  const TensorMemoryInfo& tmemInfo() const {
+    return tmem_info_;
+  }
+
+  TensorMemoryInfo& tmemInfo() {
+    return tmem_info_;
+  }
+
   // Register a boolean Val as a predicate to validate at the run time. Optional
   // validation error messages can be given as args.
   template <typename... Args>
@@ -365,6 +374,9 @@ class GpuLower : public NonCopyable {
   // Keep track of the mbarrier used for each load/store operation
   std::unordered_map<const Expr*, TensorView*> ldst_mbarrier_map_;
 
+  // Information about tensor memory usage
+  TensorMemoryInfo tmem_info_;
+
   // Keep track of validations needed at runtime. For example, a pair of
   //! "extent mod split_factor == 0" and an error message for divisibility check
   //! for vectorization.
diff --git a/csrc/device_lower/pass/index.cpp b/csrc/device_lower/pass/index.cpp
index 17c34733a79..1c6d4688f01 100644
--- a/csrc/device_lower/pass/index.cpp
+++ b/csrc/device_lower/pass/index.cpp
@@ -2141,13 +2141,47 @@ void IndexLowering::handle(const LoadStoreOp* ldst) {
     }
 
     if (!ir_utils::isStMatrixOp(ldst)) {
-      in = lowerSrcIndex(
-          ldst->in(),
-          ldst->out(),
-          {},
-          ir_utils::isLdMatrixOp(ldst) || ir_utils::isCpAsyncOp(ldst));
-      out =
-          lowerDstIndex(ldst->out(), {}, ir_utils::isCpAsyncOp(ldst), as_type);
+      bool is_ldst_tmem = ldst->opType() == LoadStoreOpType::LdTMem ||
+          ldst->opType() == LoadStoreOpType::StTMem;
+      if (is_ldst_tmem) {
+        // TODO: support other types
+        NVF_ERROR(
+            dataTypeSize(ldst->in()->dtype()) == 4,
+            "For now, we only support 32-bit types in tmem");
+        NVF_ERROR(
+            dataTypeSize(ldst->out()->dtype()) == 4,
+            "For now, we only support 32-bit types in tmem");
+        // TODO: hard code size 1 for now.
+        // According to the specification of tcgen05.{ld,st}, the register
+        // operand must be viewed as a vector of 32-bit elements.
+        // See:
+        // https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#tensor-memory-and-register-load-store-instructions
+        as_type = ArrayType{std::make_shared<DataType>(ldst->in()->dtype()), 1};
+      }
+      if (auto tv = dynamic_cast<TensorView*>(ldst->in());
+          tv != nullptr && tv->getMemoryType() == MemoryType::Tensor) {
+        // TODO: hard coded index zero for now.
+        auto index = IrBuilder::create<Val>(0, DataType::UInt32);
+        in = IrBuilder::create<kir::TensorIndex>(
+            tv, index, DataType::TMemAddress);
+      } else {
+        in = lowerSrcIndex(
+            ldst->in(),
+            ldst->out(),
+            {},
+            ir_utils::isLdMatrixOp(ldst) || ir_utils::isCpAsyncOp(ldst),
+            as_type);
+      }
+      if (auto tv = dynamic_cast<TensorView*>(ldst->out());
+          tv != nullptr && tv->getMemoryType() == MemoryType::Tensor) {
+        // TODO: hard coded index zero for now.
+        auto index = IrBuilder::create<Val>(0, DataType::UInt32);
+        out = IrBuilder::create<kir::TensorIndex>(
+            tv, index, DataType::TMemAddress);
+      } else {
+        out = lowerDstIndex(
+            ldst->out(), {}, ir_utils::isCpAsyncOp(ldst), as_type);
+      }
     }
     auto new_ldst =
         IrBuilder::create<LoadStoreOp>(ldst->opType(), out, in, ldst->cacheOp())
diff --git a/csrc/device_lower/pass/inline_ptx.cpp b/csrc/device_lower/pass/inline_ptx.cpp
index c27fd5294f6..44ee4223167 100644
--- a/csrc/device_lower/pass/inline_ptx.cpp
+++ b/csrc/device_lower/pass/inline_ptx.cpp
@@ -119,6 +119,41 @@ class LowerToInlinePtx : public kir::ExprMutator {
                   IrBuilder::create<Val>(vec_size),
                   invertedPredicate(ldst->predicate())},
               kir::Asm::Options{/*volatile=*/true}));
+    } else if (ldst->opType() == LoadStoreOpType::LdTMem) {
+      // TODO: support other types of ld/st
+      auto ptx = "tcgen05.ld.sync.aligned.32x32b.x1.b32";
+      registerReplace(
+          ldst,
+          IrBuilder::create<kir::Asm>(
+              ptx,
+              std::vector<Val*>{ldst->out()},
+              std::vector<Val*>{ldst->in()}));
+      auto wait_ptx = "tcgen05.wait::ld.sync.aligned";
+      registerInsertAfter(
+          ldst,
+          IrBuilder::create<kir::Asm>(
+              wait_ptx,
+              std::vector<Val*>{},
+              std::vector<Val*>{},
+              kir::Asm::Options{/*volatile=*/true}));
+    } else if (ldst->opType() == LoadStoreOpType::StTMem) {
+      // TODO: support other types of ld/st
+      auto ptx = "tcgen05.st.sync.aligned.32x32b.x1.b32";
+      registerReplace(
+          ldst,
+          IrBuilder::create<kir::Asm>(
+              ptx,
+              std::vector<Val*>{},
+              std::vector<Val*>{ldst->out(), ldst->in()},
+              kir::Asm::Options{/*volatile=*/true}));
+      auto wait_ptx = "tcgen05.wait::st.sync.aligned";
+      registerInsertAfter(
+          ldst,
+          IrBuilder::create<kir::Asm>(
+              wait_ptx,
+              std::vector<Val*>{},
+              std::vector<Val*>{},
+              kir::Asm::Options{/*volatile=*/true}));
     }
   }
 
diff --git a/csrc/kernel_ir.cpp b/csrc/kernel_ir.cpp
index 49ce8f820c8..ea5c5441985 100644
--- a/csrc/kernel_ir.cpp
+++ b/csrc/kernel_ir.cpp
@@ -100,7 +100,9 @@ TensorIndex::TensorIndex(
       isPointerType(index->dtype()) || index->dtype() == DataType::Index ||
           isStructType(index->dtype()) ||
           index->dtype() ==
-              DataType::UInt64 /*For matrix descriptor for hopper MMA*/,
+              DataType::UInt64 /*For matrix descriptor for hopper MMA*/
+          || index->dtype() ==
+              DataType::UInt32 /*Temporarily enabled for TMem tensor*/,
       "Cannot index with a value other than an int/pointer/struct.");
 }
 
diff --git a/csrc/type.cpp b/csrc/type.cpp
index 06056f6f927..eaf18ae5e2a 100644
--- a/csrc/type.cpp
+++ b/csrc/type.cpp
@@ -243,11 +243,11 @@ static std::string data_type2string(DataType t) {
             case DataType::UInt16:
               return "uint16_t";
             case DataType::UInt32:
+            case DataType::SMemAddress:
+            case DataType::TMemAddress:
               return "uint32_t";
             case DataType::UInt64:
               return "uint64_t";
-            case DataType::SMemAddress:
-              return "unsigned";
             case DataType::ComplexFloat:
               return "std::complex<float>";
             case DataType::ComplexDouble:
@@ -860,6 +860,10 @@ const char* load_store_type2string(LoadStoreOpType t) {
       return "CpAsyncBulk";
     case LoadStoreOpType::CpAsyncBulkTensorTile:
       return "CpAsyncBulkTensorTile";
+    case LoadStoreOpType::LdTMem:
+      return "LdTMem";
+    case LoadStoreOpType::StTMem:
+      return "StTMem";
     default:
       NVF_THROW("Unexpected parallel type");
   }
diff --git a/csrc/type.h b/csrc/type.h
index 388d0bb05b2..cf577bee188 100644
--- a/csrc/type.h
+++ b/csrc/type.h
@@ -88,6 +88,7 @@ enum class PrimDataType {
   ComplexFloat,
   // Pointers
   SMemAddress,
+  TMemAddress,
   // Null
   Null
 };
@@ -196,6 +197,7 @@ struct DataType {
   static constexpr PrimDataType ComplexFloat = PrimDataType::ComplexFloat;
   static constexpr PrimDataType ComplexDouble = PrimDataType::ComplexDouble;
   static constexpr PrimDataType SMemAddress = PrimDataType::SMemAddress;
+  static constexpr PrimDataType TMemAddress = PrimDataType::TMemAddress;
   static constexpr PrimDataType Null = PrimDataType::Null;
 };
 
@@ -297,7 +299,7 @@ inline bool isUnsignedIntegralType(DataType dtype) {
 // Returns if the datatype is a pointer type
 inline bool isPointerType(DataType dtype) {
   return std::holds_alternative<PointerType>(dtype.type) ||
-      dtype == DataType::SMemAddress;
+      dtype == DataType::SMemAddress || dtype == DataType::TMemAddress;
 }
 
 // Returns if the datatype is an integer or pointer type
@@ -801,7 +803,9 @@ enum class LoadStoreOpType {
   CpAsync,
   CpAsyncBulk,
   CpAsyncBulkTensorTile,
-  StMatrix
+  StMatrix,
+  LdTMem,
+  StTMem
 };
 
 // Used to label what part of the circular buffered iterdomain
@@ -1055,11 +1059,11 @@ constexpr inline size_t primDataTypeSize(PrimDataType type) {
     case DataType::UInt16:
       return sizeof(uint16_t);
     case DataType::UInt32:
+    case DataType::SMemAddress:
+    case DataType::TMemAddress:
       return sizeof(uint32_t);
     case DataType::UInt64:
       return sizeof(uint64_t);
-    case DataType::SMemAddress:
-      return sizeof(unsigned);
     default:
       NVF_THROW("Size undefined for data type.");
   }
diff --git a/tests/cpp/test_loop_rotation.cpp b/tests/cpp/test_loop_rotation.cpp
index d8ae9d49bda..9fb4c440070 100644
--- a/tests/cpp/test_loop_rotation.cpp
+++ b/tests/cpp/test_loop_rotation.cpp
@@ -568,7 +568,7 @@ __global__ void CUDAGeneratedKernel(Tensor<float, 2, 2> T0, Tensor<float, 2, 2>
   const unsigned smem_offset = 0;
   NVFUSER_DEFINE_MAGIC_ZERO;
   float* T4 = reinterpret_cast<float*>(array + smem_offset + 0LL);
-  unsigned i0;
+  uint32_t i0;
   i0 = toSmem(T4);
   float* ptr1;
   ptr1 = T0.data + (4LL * T0.alloc_stride[0LL]);
@@ -576,7 +576,7 @@ __global__ void CUDAGeneratedKernel(Tensor<float, 2, 2> T0, Tensor<float, 2, 2>
   for(nvfuser_index_t i2 = 0LL; i2 < 4LL; ++i2) {
     float* ptr3;
     ptr3 = T0.data + (T0.alloc_stride[0LL] * i2);
-    unsigned i4;
+    uint32_t i4;
     i4 = i0 + (12LL * i2);
     bool b5;
     b5 = (i2 + nvfuser_zero) < T0.logical_size[0LL];
@@ -608,7 +608,7 @@ __global__ void CUDAGeneratedKernel(Tensor<float, 2, 2> T0, Tensor<float, 2, 2>
     ptr8 = ptr1 + (T0.alloc_stride[0LL] * i7);
     nvfuser_index_t i9;
     i9 = 4LL + i7;
-    unsigned i10;
+    uint32_t i10;
     i10 = i0 + (12LL * (i9 % 5LL));
     nvfuser_index_t i11;
     i11 = 1LL + (3LL * (i7 % 5LL));
diff --git a/tests/cpp/test_memory.cpp b/tests/cpp/test_memory.cpp
index d25a8420440..bfdb4de4807 100644
--- a/tests/cpp/test_memory.cpp
+++ b/tests/cpp/test_memory.cpp
@@ -2761,6 +2761,45 @@ TEST_F(TMADocTest, Figure15e) {
 
 // End TMA tests
 
+// Tensor memory tests
+using TMemTest = BlackwellBase;
+
+TEST_F(TMemTest, GmemRegTMemRegGmemCopy) {
+  Fusion fusion;
+  FusionGuard fg(&fusion);
+
+  auto tv0 = makeSymbolicTensor(1);
+  fusion.addInput(tv0);
+  auto tv1 = set(tv0); // register
+  auto tv2 = set(tv1); // tmem
+  auto tv3 = set(tv2); // register
+  auto tv4 = set(tv3); // gmem
+  fusion.addOutput(tv4);
+
+  tv2->setMemoryType(MemoryType::Tensor);
+  tv2->definition()->as<LoadStoreOp>()->setOpType(LoadStoreOpType::StTMem);
+  tv3->definition()->as<LoadStoreOp>()->setOpType(LoadStoreOpType::LdTMem);
+
+  tv4->split(0, 32);
+
+  TransformPropagator propagator(tv4);
+  MaxLogicalDomainInfoSpanningTree(tv4).traverse(&propagator);
+
+  tv4->axis(0)->parallelize(ParallelType::BIDx);
+  tv4->axis(1)->parallelize(ParallelType::TIDx);
+
+  scheduler_utils::parallelizeAllLike(tv4, {tv1, tv2, tv3});
+
+  inlineMost();
+
+  KernelExecutor ke;
+  ke.compile(&fusion);
+  auto t0 = at::randn(
+      {12800}, at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0));
+  auto cg_outputs = ke.run({t0});
+  testValidate(&fusion, cg_outputs, {t0}, {t0}, __LINE__, __FILE__);
+}
+
 using LdMatrixTestParam = std::tuple<MmaMacro, MmaOperand>;
 
 class LdMatrixTest : public NVFuserFixtureParamTest<LdMatrixTestParam> {
diff --git a/tests/cpp/utils.h b/tests/cpp/utils.h
index 6bfb74ac1c5..63ffdaaffc4 100644
--- a/tests/cpp/utils.h
+++ b/tests/cpp/utils.h
@@ -620,6 +620,16 @@ class HopperBase : public NVFuserTest {
   }
 };
 
+class BlackwellBase : public NVFuserTest {
+ protected:
+  void SetUp() override {
+    if (cudaArchGuardShouldSkip(10, 0)) {
+      GTEST_SKIP() << "skipping tests on non-Blackwell GPUs";
+    }
+    NVFuserTest::SetUp();
+  }
+};
+
 // TMA is supported on Hopper and newer GPUs
 class TmaBase : public NVFuserTest {
  protected: