diff --git a/tensorflow/compiler/tf2tensorrt/convert/convert_nodes_test.cc b/tensorflow/compiler/tf2tensorrt/convert/convert_nodes_test.cc
index 6c2b8fdc091..ef03ab91714 100644
--- a/tensorflow/compiler/tf2tensorrt/convert/convert_nodes_test.cc
+++ b/tensorflow/compiler/tf2tensorrt/convert/convert_nodes_test.cc
@@ -654,9 +654,8 @@ class ConverterTest : public ::testing::Test {
   ConverterTest() { Reset(); }
 
   void Reset() {
-    builder_.reset(nvinfer1::createInferBuilder(logger_));
     converter_ =
-        std::move(Converter::Create(builder_.get(), TrtPrecisionMode::FP32,
+        std::move(Converter::Create(TrtPrecisionMode::FP32,
                                     /*use_calibration=*/false, &logger_)
                       .ValueOrDie());
     weight_store_ = &converter_->weight_store_;
@@ -702,9 +701,6 @@ class ConverterTest : public ::testing::Test {
 
  private:
   Logger logger_;
-  // These members are ordered in a way such that the destruction order is:
-  // converter_ -> builder_
-  TrtUniquePtrType<nvinfer1::IBuilder> builder_;
 
  protected:
   std::unique_ptr<Converter> converter_;
@@ -996,9 +992,7 @@ TEST_F(ConverterTest, MaybeApplyQuantizationRanges) {
   FakeITensor input, infer_1, infer_2, infer_3;
   FakeITensor not_infer;
   Logger logger;
-  TrtUniquePtrType<nvinfer1::IBuilder> builder(
-      nvinfer1::createInferBuilder(logger));
-  auto int8_converter = Converter::Create(builder.get(), TrtPrecisionMode::INT8,
+  auto int8_converter = Converter::Create(TrtPrecisionMode::INT8,
                                           /*use_calibration=*/true, &logger)
                             .ValueOrDie();
   int8_converter->ProvideQuantizationRange(&input, -5.0f, 5.0f);
@@ -1255,12 +1249,8 @@ class OpConverterTest : public ::testing::Test {
     engine_.reset(nullptr);
 
     // Re-create them in proper order.
-    builder_.reset(nvinfer1::createInferBuilder(logger_));
-    builder_->setMaxWorkspaceSize(1 << 26);
-
-    // Reset the converter.
     converter_ =
-        std::move(Converter::Create(builder_.get(), precision_mode_to_test_,
+        std::move(Converter::Create(precision_mode_to_test_,
                                     /*use_calibration=*/false, &logger_)
                       .ValueOrDie());
 
@@ -1294,18 +1284,13 @@ class OpConverterTest : public ::testing::Test {
     TF_EXPECT_OK(converter_->RenameAndMarkOutputTensors(output_info));
 
     // Build the TRT engine.
-    if (precision_mode == TrtPrecisionMode::FP16) {
-      builder_->setFp16Mode(true);
-    } else if (precision_mode == TrtPrecisionMode::INT8) {
-      // Setting FP16 mode as well allows TRT to also consider FP16 kernels and
-      // use them in situations where they are faster than INT8 or where INT8 is
-      // not supported for a given layer.
-      builder_->setFp16Mode(true);
-      builder_->setInt8Mode(true);
-    }
     ASSERT_EQ(nullptr, engine_.get());
-    builder_->setMaxBatchSize(batch_size);
-    TF_ASSERT_OK(converter_->BuildCudaEngine(&engine_));
+    TF_ASSERT_OK(
+        converter_->BuildCudaEngine(&engine_,
+                                    /*max_batch_size=*/batch_size,
+                                    /*max_workspace_size_bytes=*/1 << 26,
+                                    /*allocator=*/nullptr,
+                                    /*calibrator=*/nullptr));
     CHECK_NOTNULL(engine_.get());
     CheckDataTypeMatches(input_data);
     CheckDataTypeMatches(*output_data);
@@ -1473,7 +1458,6 @@ class OpConverterTest : public ::testing::Test {
 
  private:
   Logger logger_;
-  TrtUniquePtrType<nvinfer1::IBuilder> builder_;
   TrtUniquePtrType<nvinfer1::ICudaEngine> engine_;
   cudaStream_t stream_;
   // Used to create placeholders with shape and data type information. The