diff --git a/mlir/lib/Dialect/Tensor/IR/TensorDialect.cpp b/mlir/lib/Dialect/Tensor/IR/TensorDialect.cpp
index 8af087cbf0f61..e7d8f52d309c9 100644
--- a/mlir/lib/Dialect/Tensor/IR/TensorDialect.cpp
+++ b/mlir/lib/Dialect/Tensor/IR/TensorDialect.cpp
@@ -49,8 +49,8 @@ void TensorDialect::initialize() {
       >();
   addInterfaces<TensorInlinerInterface>();
   declarePromisedInterfaces<
-      bufferization::BufferizableOpInterface, CastOp, CollapseShapeOp, DimOp,
-      EmptyOp, ExpandShapeOp, ExtractSliceOp, ExtractOp, FromElementsOp,
+      bufferization::BufferizableOpInterface, CastOp, CollapseShapeOp, ConcatOp,
+      DimOp, EmptyOp, ExpandShapeOp, ExtractSliceOp, ExtractOp, FromElementsOp,
       GenerateOp, InsertOp, InsertSliceOp, PadOp, ParallelInsertSliceOp, RankOp,
       ReshapeOp, SplatOp>();
   declarePromisedInterfaces<transform::FindPayloadReplacementOpInterface,
diff --git a/mlir/lib/Dialect/Tensor/Transforms/BufferizableOpInterfaceImpl.cpp b/mlir/lib/Dialect/Tensor/Transforms/BufferizableOpInterfaceImpl.cpp
index c0e697292d2a0..6525e58d002a2 100644
--- a/mlir/lib/Dialect/Tensor/Transforms/BufferizableOpInterfaceImpl.cpp
+++ b/mlir/lib/Dialect/Tensor/Transforms/BufferizableOpInterfaceImpl.cpp
@@ -1048,6 +1048,134 @@ struct SplatOpInterface
   }
 };
 
+/// Bufferization of tensor.concat. Bufferizes to a new allocation that is
+/// filled with copy ops. Similar to tensor.from_elements, but using memref.copy
+/// on subviews instead of memref.store.
+struct ConcatOpInterface
+    : public BufferizableOpInterface::ExternalModel<ConcatOpInterface,
+                                                    tensor::ConcatOp> {
+
+  bool bufferizesToAllocation(Operation *op, Value value) const { return true; }
+
+  bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
+                               const AnalysisState &state) const {
+    return false;
+  }
+
+  bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
+                              const AnalysisState &state) const {
+    return true;
+  }
+
+  AliasingValueList getAliasingValues(Operation *op, OpOperand &opOperand,
+                                      const AnalysisState &state) const {
+    return {};
+  }
+
+  LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
+                          const BufferizationOptions &options) const {
+    OpBuilder::InsertionGuard g(rewriter);
+    auto concatOp = cast<tensor::ConcatOp>(op);
+
+    // Allocate memory.
+    Location loc = op->getLoc();
+    FailureOr<Value> tensorAlloc = allocateTensorForShapedValue(
+        rewriter, loc, concatOp.getResult(), options,
+        /*copy=*/false);
+    if (failed(tensorAlloc))
+      return failure();
+    auto tensorType = cast<RankedTensorType>(tensorAlloc->getType());
+
+    // TODO: Implement memory space for this op.
+    if (options.defaultMemorySpaceFn(tensorType) != Attribute())
+      return op->emitError("memory space not implemented yet");
+
+    MemRefLayoutAttrInterface layout;
+    MemRefType memrefType =
+        MemRefType::get(concatOp.getResultType().getShape(),
+                        concatOp.getResultType().getElementType(), layout);
+    Value dstBuffer = rewriter.create<bufferization::ToBufferOp>(
+        op->getLoc(), memrefType, *tensorAlloc);
+
+    // Extract the dimension for the concat op
+    uint64_t concatDim = concatOp.getDim();
+    bool dynamicConcatDim = false;
+
+    SmallVector<OpFoldResult> offsets(tensorType.getRank(),
+                                      rewriter.getIndexAttr(0));
+    SmallVector<OpFoldResult> strides(tensorType.getRank(),
+                                      rewriter.getIndexAttr(1));
+    SmallVector<OpFoldResult> sizes;
+
+    for (const auto &[dimIdx, dimSize] :
+         llvm::enumerate(tensorType.getShape())) {
+      if (dimSize == ShapedType::kDynamic) {
+        auto dimOp = rewriter.create<memref::DimOp>(loc, dstBuffer, dimIdx);
+        sizes.push_back(dimOp.getResult());
+        if (dimIdx == concatDim)
+          dynamicConcatDim = true;
+      } else {
+        sizes.push_back(rewriter.getIndexAttr(dimSize));
+      }
+    }
+
+    int64_t concatDimOffset = 0;
+    std::optional<Value> dynamicOffset;
+    std::optional<Value> dynamicSize;
+    if (dynamicConcatDim) {
+      // One or more operands have dynamic size, so we must accumulate the
+      // offset with arith ops.
+      dynamicOffset = rewriter.create<arith::ConstantIndexOp>(loc, 0);
+    }
+
+    for (auto operand : concatOp.getInputs()) {
+      // Get the buffer for the operand.
+      FailureOr<Value> srcBuffer = getBuffer(rewriter, operand, options);
+      if (failed(srcBuffer))
+        return failure();
+
+      // Each operand may have a different size along the concat dimension,
+      // so the offset on that axis must accumulate through the loop, and the
+      // size must change to the size of the current operand.
+      auto operandTensorType = cast<RankedTensorType>(operand.getType());
+      int64_t operandConcatDimSize = operandTensorType.getDimSize(concatDim);
+
+      if (dynamicConcatDim) {
+        offsets[concatDim] = dynamicOffset.value();
+        dynamicSize = rewriter.create<memref::DimOp>(loc, *srcBuffer, concatDim)
+                          .getResult();
+        sizes[concatDim] = dynamicSize.value();
+      } else {
+        sizes[concatDim] = rewriter.getIndexAttr(operandConcatDimSize);
+        offsets[concatDim] = rewriter.getIndexAttr(concatDimOffset);
+      }
+
+      // Create a subview of the destination buffer.
+      auto dstMemrefType = cast<MemRefType>(memrefType);
+      MemRefType subviewMemRefType =
+          memref::SubViewOp::inferRankReducedResultType(
+              operandTensorType.getShape(), dstMemrefType, offsets, sizes,
+              strides);
+      Value subview = rewriter.create<memref::SubViewOp>(
+          loc, subviewMemRefType, dstBuffer, offsets, sizes, strides);
+
+      // Copy the source buffer into the destination subview.
+      if (failed(options.createMemCpy(rewriter, loc, *srcBuffer, subview)))
+        return failure();
+
+      if (dynamicConcatDim) {
+        dynamicOffset = rewriter.create<arith::AddIOp>(
+            loc, dynamicOffset.value(), dynamicSize.value());
+      } else {
+        concatDimOffset += operandConcatDimSize;
+      }
+    }
+
+    replaceOpWithBufferizedValues(rewriter, op, dstBuffer);
+    return success();
+  }
+};
+
 } // namespace
 } // namespace tensor
 } // namespace mlir
@@ -1057,6 +1185,7 @@ void mlir::tensor::registerBufferizableOpInterfaceExternalModels(
   registry.addExtension(+[](MLIRContext *ctx, tensor::TensorDialect *dialect) {
     CastOp::attachInterface<CastOpInterface>(*ctx);
     CollapseShapeOp::attachInterface<CollapseShapeOpInterface>(*ctx);
+    ConcatOp::attachInterface<ConcatOpInterface>(*ctx);
     DimOp::attachInterface<DimOpInterface>(*ctx);
     EmptyOp::attachInterface<EmptyOpInterface>(*ctx);
     ExpandShapeOp::attachInterface<ExpandShapeOpInterface>(*ctx);
diff --git a/mlir/test/Dialect/Tensor/bufferize.mlir b/mlir/test/Dialect/Tensor/bufferize.mlir
index 567c4abea488e..e9c3ba7e3b970 100644
--- a/mlir/test/Dialect/Tensor/bufferize.mlir
+++ b/mlir/test/Dialect/Tensor/bufferize.mlir
@@ -615,6 +615,97 @@ func.func @tensor.splat(%f: f32) -> tensor<10x2x4xf32> {
 
 // -----
 
+// CHECK-LABEL:   func @tensor.concat(
+// CHECK-SAME:        %[[F:.*]]: tensor<8xf32>)
+// CHECK:           %[[F_MEMREF:.*]] = bufferization.to_buffer %[[F]]
+// CHECK:           %[[ALLOC:.*]] = memref.alloc() {{.*}} : memref<16xf32>
+// CHECK:           %[[SUBVIEW1:.*]] = memref.subview %[[ALLOC]][0] [8] [1]
+// CHECK:           memref.copy %[[F_MEMREF]], %[[SUBVIEW1]]
+// CHECK:           %[[SUBVIEW2:.*]] = memref.subview %[[ALLOC]][8] [8] [1]
+// CHECK:           memref.copy %[[F_MEMREF]], %[[SUBVIEW2]]
+// CHECK:           %[[RET:.*]] = bufferization.to_tensor %[[ALLOC]]
+// CHECK:           return %[[RET]]
+// CHECK:         }
+func.func @tensor.concat(%f: tensor<8xf32>) -> tensor<16xf32> {
+  %t = tensor.concat dim(0) %f, %f : (tensor<8xf32>, tensor<8xf32>) -> tensor<16xf32>
+  return %t : tensor<16xf32>
+}
+
+// -----
+
+// CHECK-LABEL:   func @tensor.concat_different_shapes(
+// CHECK-SAME:        %[[F:.*]]: tensor<8x4xf32>
+// CHECK-SAME:        %[[G:.*]]: tensor<8x5xf32>
+// CHECK-DAG:       %[[F_MEMREF:.*]] = bufferization.to_buffer %[[F]]
+// CHECK-DAG:       %[[G_MEMREF:.*]] = bufferization.to_buffer %[[G]]
+// CHECK:           %[[ALLOC:.*]] = memref.alloc() {{.*}} : memref<8x9xf32>
+// CHECK:           %[[SUBVIEW1:.*]] = memref.subview %[[ALLOC]][0, 0] [8, 4] [1, 1]
+// CHECK:           memref.copy %[[F_MEMREF]], %[[SUBVIEW1]]
+// CHECK:           %[[SUBVIEW2:.*]] = memref.subview %[[ALLOC]][0, 4] [8, 5] [1, 1]
+// CHECK:           memref.copy %[[G_MEMREF]], %[[SUBVIEW2]]
+// CHECK:           %[[RET:.*]] = bufferization.to_tensor %[[ALLOC]]
+// CHECK:           return %[[RET]]
+// CHECK:         }
+func.func @tensor.concat_different_shapes(%f: tensor<8x4xf32>, %g: tensor<8x5xf32>) -> tensor<8x9xf32> {
+  %t = tensor.concat dim(1) %f, %g : (tensor<8x4xf32>, tensor<8x5xf32>) -> tensor<8x9xf32>
+  return %t : tensor<8x9xf32>
+}
+
+// -----
+
+// CHECK-LABEL:   func @tensor.concat_dynamic(
+// CHECK-SAME:        %[[F:.*]]: tensor<8x?xf32>,
+// CHECK-SAME:        %[[G:.*]]: tensor<8x?xf32>
+// CHECK-DAG:       %[[F_MEMREF:.*]] = bufferization.to_buffer %[[F]]
+// CHECK-DAG:       %[[G_MEMREF:.*]] = bufferization.to_buffer %[[G]]
+// CHECK-DAG:       %[[c1:.*]] = arith.constant 1 : index
+// CHECK-DAG:       %[[F_DIM:.*]] = memref.dim %[[F_MEMREF]], %[[c1]]
+// CHECK-DAG:       %[[G_DIM:.*]] = memref.dim %[[G_MEMREF]], %[[c1]]
+// CHECK:           %[[ALLOC:.*]] = memref.alloc
+// CHECK-SAME:                                    memref<8x?xf32>
+// CHECK-DAG:       %[[OFFSET:.*]] = arith.constant 0 : index
+// CHECK:           %[[SUBVIEW1:.*]] = memref.subview %[[ALLOC]][0, %[[OFFSET]]] [8, %[[F_DIM]]] [1, 1]
+// CHECK:           memref.copy %[[F_MEMREF]], %[[SUBVIEW1]]
+// CHECK:           %[[OFFSET_2:.*]] = arith.addi %[[OFFSET]], %[[F_DIM]] : index
+// CHECK:           %[[SUBVIEW2:.*]] = memref.subview %[[ALLOC]][0, %[[OFFSET_2]]] [8, %[[G_DIM]]] [1, 1]
+// CHECK:           memref.copy %[[G_MEMREF]], %[[SUBVIEW2]]
+// CHECK:           %[[RET:.*]] = bufferization.to_tensor %[[ALLOC]]
+// CHECK:           return %[[RET]]
+// CHECK:         }
+func.func @tensor.concat_dynamic(%f: tensor<8x?xf32>, %g: tensor<8x?xf32>) -> tensor<8x?xf32> {
+  %t = tensor.concat dim(1) %f, %g : (tensor<8x?xf32>, tensor<8x?xf32>) -> tensor<8x?xf32>
+  return %t : tensor<8x?xf32>
+}
+
+// -----
+
+// CHECK-LABEL:   func @tensor.concat_dynamic_nonconcat_dim(
+// CHECK-SAME:        %[[F:.*]]: tensor<?x?xf32>,
+// CHECK-SAME:        %[[G:.*]]: tensor<?x?xf32>
+// CHECK-DAG:       %[[F_MEMREF:.*]] = bufferization.to_buffer %[[F]]
+// CHECK-DAG:       %[[G_MEMREF:.*]] = bufferization.to_buffer %[[G]]
+// CHECK-DAG:       %[[c1:.*]] = arith.constant 1 : index
+// CHECK-DAG:       %[[c0:.*]] = arith.constant 0 : index
+// CHECK-DAG:       %[[F_DIM:.*]] = memref.dim %[[F_MEMREF]], %[[c1]]
+// CHECK-DAG:       %[[G_DIM:.*]] = memref.dim %[[G_MEMREF]], %[[c1]]
+// CHECK:           %[[ALLOC:.*]] = memref.alloc
+// CHECK-SAME:                                    memref<?x?xf32>
+// CHECK-DAG:       %[[NON_CONCAT_DIM:.*]] = memref.dim %[[ALLOC]], %[[c0]]
+// CHECK:           %[[SUBVIEW1:.*]] = memref.subview %[[ALLOC]][0, %[[c0]]] [%[[NON_CONCAT_DIM]], %[[F_DIM]]] [1, 1]
+// CHECK:           memref.copy %[[F_MEMREF]], %[[SUBVIEW1]]
+// CHECK:           %[[OFFSET_2:.*]] = arith.addi %[[c0]], %[[F_DIM]] : index
+// CHECK:           %[[SUBVIEW2:.*]] = memref.subview %[[ALLOC]][0, %[[OFFSET_2]]] [%[[NON_CONCAT_DIM]], %[[G_DIM]]] [1, 1]
+// CHECK:           memref.copy %[[G_MEMREF]], %[[SUBVIEW2]]
+// CHECK:           %[[RET:.*]] = bufferization.to_tensor %[[ALLOC]]
+// CHECK:           return %[[RET]]
+// CHECK:         }
+func.func @tensor.concat_dynamic_nonconcat_dim(%f: tensor<?x?xf32>, %g: tensor<?x?xf32>) -> tensor<?x?xf32> {
+  %t = tensor.concat dim(1) %f, %g : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
+  return %t : tensor<?x?xf32>
+}
+
+// -----
+
 // CHECK-LABEL: func @tensor.splat_dynamic(
 // CHECK-SAME:  %[[F:[a-zA-Z0-9_]+]]: f32
 // CHECK-SAME:  %[[M:[a-zA-Z0-9_]+]]: index