diff --git a/lib/TPP/Transforms/TileConsumerAndFuseProducers.cpp b/lib/TPP/Transforms/TileConsumerAndFuseProducers.cpp
index b7804bfee..bc46c323d 100644
--- a/lib/TPP/Transforms/TileConsumerAndFuseProducers.cpp
+++ b/lib/TPP/Transforms/TileConsumerAndFuseProducers.cpp
@@ -449,7 +449,32 @@ static FailureOr<scf::SCFTileAndFuseResult> fuseWithEltwise(
 // Trivial tile selection. If the dimension is statically known, it perfectly
 // divides the tile, and we have enough iterations return a default of 32.
 static int64_t getTileForDim(linalg::LinalgOp linalgOp, unsigned dim) {
-  const int64_t tile = 32;
+  int64_t tile = 32;
+
+  // Check if a tile size hint is associated to the IR via DLTI.
+  auto deriveFromDLTI = [&](ModuleOp moduleOp) {
+    if (!moduleOp)
+      return;
+    TargetSystemSpecInterface sysSpec = moduleOp.getTargetSystemSpec();
+    if (!sysSpec)
+      return;
+    auto deviceId = StringAttr::get(linalgOp->getContext(), "CPU");
+    auto deviceSpec = sysSpec.getDeviceSpecForDeviceID(deviceId);
+    if (!deviceSpec)
+      return;
+    auto tileSizeId = StringAttr::get(linalgOp->getContext(), "tile_size");
+    DataLayoutEntryInterface entry =
+        (*deviceSpec).getSpecForIdentifier(tileSizeId);
+    if (!entry)
+      return;
+    Attribute value = entry.getValue();
+    if (auto intAttr = llvm::dyn_cast<IntegerAttr>(value))
+      tile = intAttr.getInt();
+    // TODO: might want to print a warning if tile_size exists as a key but the
+    //       associated attribute has an unexpected type.
+  };
+  deriveFromDLTI(linalgOp->getParentOfType<mlir::ModuleOp>());
+
   SmallVector<int64_t, 4> loopsRange = linalgOp.getStaticLoopRanges();
   if (loopsRange[dim] == ShapedType::kDynamic)
     return tile;
diff --git a/test/Passes/tile-and-fuse-default.mlir b/test/Passes/tile-and-fuse-default.mlir
index 3a1bf133c..d1e5f1079 100644
--- a/test/Passes/tile-and-fuse-default.mlir
+++ b/test/Passes/tile-and-fuse-default.mlir
@@ -729,3 +729,58 @@ func.func @contraction(%arg0: tensor<16x1xf32>, %arg1: tensor<1x32xf32>) -> tens
 
 // CHECK-LABEL: contraction
 // CHECK-NOT: scf.for
+
+// -----
+
+// CHECK-LABEL: dlti_tile_size_32
+module attributes { dlti.target_system_spec = #dlti.target_system_spec<"CPU" : #dlti.target_device_spec<#dlti.dl_entry<"tile_size", 32 : i32>>> } {
+  func.func @dlti_tile_size_32(%arg0: tensor<2048x2048xf32>, %arg1: tensor<2048x2048xf32>, %arg2: tensor<2048x2048xf32>)
+      -> tensor<2048x2048xf32> {
+    // CHECK-DAG: %[[C32:.+]] = arith.constant 32 : index
+    // CHECK: scf.for %{{.+}} step %[[C32]]
+    // CHECK-NEXT: scf.for %{{.+}} step %[[C32]]
+    // CHECK: %{{.+}} = linalg.matmul ins(%{{.+}}, %{{.+}} : tensor<32x2048xf32>, tensor<2048x32xf32>)
+    // CHECK-SAME:                    outs(%{{.+}} : tensor<32x32xf32>)
+    %0 = linalg.matmul ins(%arg0, %arg1: tensor<2048x2048xf32>, tensor<2048x2048xf32>)
+                       outs(%arg2: tensor<2048x2048xf32>)
+      -> tensor<2048x2048xf32>
+    return %0 : tensor<2048x2048xf32>
+  }
+}
+
+// -----
+
+// CHECK-LABEL: dlti_tile_size_64
+module attributes { dlti.target_system_spec = #dlti.target_system_spec<"CPU" : #dlti.target_device_spec<#dlti.dl_entry<"tile_size", 64 : i32>>> } {
+  func.func @dlti_tile_size_64(%arg0: tensor<2048x2048xf32>, %arg1: tensor<2048x2048xf32>, %arg2: tensor<2048x2048xf32>)
+      -> tensor<2048x2048xf32> {
+    // CHECK-DAG: %[[C64:.+]] = arith.constant 64 : index
+    // CHECK: scf.for %{{.+}} step %[[C64]]
+    // CHECK-NEXT: scf.for %{{.+}} step %[[C64]]
+    // CHECK: %{{.+}} = linalg.matmul ins(%{{.+}}, %{{.+}} : tensor<64x2048xf32>, tensor<2048x64xf32>)
+    // CHECK-SAME:                    outs(%{{.+}} : tensor<64x64xf32>)
+    %0 = linalg.matmul ins(%arg0, %arg1: tensor<2048x2048xf32>, tensor<2048x2048xf32>)
+                       outs(%arg2: tensor<2048x2048xf32>)
+      -> tensor<2048x2048xf32>
+    return %0 : tensor<2048x2048xf32>
+  }
+}
+
+
+// -----
+
+// CHECK-LABEL: dlti_tile_size_16
+module attributes { dlti.target_system_spec = #dlti.target_system_spec<"CPU" : #dlti.target_device_spec<#dlti.dl_entry<"tile_size", 16 : i32>>> } {
+  func.func @dlti_tile_size_16(%arg0: tensor<2048x2048xf32>, %arg1: tensor<2048x2048xf32>, %arg2: tensor<2048x2048xf32>)
+      -> tensor<2048x2048xf32> {
+    // CHECK-DAG: %[[C16:.+]] = arith.constant 16 : index
+    // CHECK: scf.for %{{.+}} step %[[C16]]
+    // CHECK-NEXT: scf.for %{{.+}} step %[[C16]]
+    // CHECK: %{{.+}} = linalg.matmul ins(%{{.+}}, %{{.+}} : tensor<16x2048xf32>, tensor<2048x16xf32>)
+    // CHECK-SAME:                    outs(%{{.+}} : tensor<16x16xf32>)
+    %0 = linalg.matmul ins(%arg0, %arg1: tensor<2048x2048xf32>, tensor<2048x2048xf32>)
+                       outs(%arg2: tensor<2048x2048xf32>)
+      -> tensor<2048x2048xf32>
+    return %0 : tensor<2048x2048xf32>
+  }
+}