diff --git a/core/conversion/converters/impl/quantization.cpp b/core/conversion/converters/impl/quantization.cpp
index e8fdc69f84..addf629e6b 100644
--- a/core/conversion/converters/impl/quantization.cpp
+++ b/core/conversion/converters/impl/quantization.cpp
@@ -11,6 +11,22 @@ namespace {
 
 #if NV_TENSORRT_MAJOR > 7
 // clang-format off
+
+bool add_qdq(ConversionCtx *ctx, const torch::jit::Node* n, nvinfer1::ITensor* input, nvinfer1::ITensor* scale, std::string& opName) {
+  nvinfer1::IQuantizeLayer* quantize_layer = ctx->net->addQuantize(*input, *scale);
+  TORCHTRT_CHECK(quantize_layer, "Unable to create QuantizeLayer from node: " << *n);
+  quantize_layer->setAxis(0);
+
+  nvinfer1::IDequantizeLayer* dequantize_layer = ctx->net->addDequantize(*quantize_layer->getOutput(0), *scale);
+  TORCHTRT_CHECK(dequantize_layer, "Unable to create DequantizeLayer from node: " << *n);
+  dequantize_layer->setAxis(0);
+
+  auto qdq_out = ctx->AssociateValueAndTensor(n->outputs()[0], dequantize_layer->getOutput(0));
+  LOG_DEBUG("[" << opName << "]"<< " Output tensor shape: " << qdq_out->getDimensions());
+
+  return true;
+}
+
 auto quantization_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns()
   .pattern({"aten::fake_quantize_per_tensor_affine(Tensor self, float scale, int zero_point, int quant_min, int quant_max) -> (Tensor)",
             [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
@@ -20,18 +36,16 @@ auto quantization_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns
               auto scale = args[1].unwrapToScalar().to<float>();
               auto scaleTensor = tensor_to_const(ctx, torch::tensor({scale}));
               // Add and configure a QuantizeLayer.
-              nvinfer1::IQuantizeLayer* quantize_layer = ctx->net->addQuantize(*input, *scaleTensor);
-              quantize_layer->setAxis(0);
-
-              // Add and configure DequantizeLayer following a QuantizeLayer
-              nvinfer1::IDequantizeLayer* dequantize_layer = ctx->net->addDequantize(*quantize_layer->getOutput(0), *scaleTensor);
-              dequantize_layer->setAxis(0);
-
-              auto qdq_out = ctx->AssociateValueAndTensor(n->outputs()[0], dequantize_layer->getOutput(0));
-              LOG_DEBUG("[fake_quantize_per_tensor_affine] Output tensor shape: " << qdq_out->getDimensions());
-
-              return true;
+              std::string opName("aten::fake_quantize_per_tensor_affine");
+              return add_qdq(ctx, n, input, scaleTensor, opName);
             }})
+  .pattern({"aten::fake_quantize_per_tensor_affine.tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max) -> (Tensor)",
+             [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+              auto input = args[0].ITensorOrFreeze(ctx);
+              auto scale = args[1].ITensorOrFreeze(ctx);
+              std::string opName("aten::fake_quantize_per_tensor_affine.tensor_qparams");
+              return add_qdq(ctx, n, input, scale, opName);
+           }})
   .pattern({"aten::fake_quantize_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max) -> (Tensor)",
             [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
               // This aten operator is generated from torch.fake_quantize_per_channel_affine op in Pytorch python API.
diff --git a/tests/core/conversion/converters/test_quantization.cpp b/tests/core/conversion/converters/test_quantization.cpp
index fcbef02e16..d6881bb37e 100644
--- a/tests/core/conversion/converters/test_quantization.cpp
+++ b/tests/core/conversion/converters/test_quantization.cpp
@@ -30,6 +30,40 @@ TEST(Converters, ATenFakeQuantizePerTensorConvertsCorrectly) {
       torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6));
 }
 
+TEST(Converters, ATenFakeQuantizePerTensorWithParamsConvertsCorrectly) {
+  const auto graph = R"IR(
+    graph(%x.1 : Tensor):
+        %22 : int = prim::Constant[value=-128]()
+        %14 : int = prim::Constant[value=4]()
+        %9 : None = prim::Constant()
+        %35 : Device = prim::Constant[value="cuda:0"]()
+        %6 : int = prim::Constant[value=6]()
+        %7 : int = prim::Constant[value=3]()
+        %3 : int = prim::Constant[value=1]()
+        %5 : float = prim::Constant[value=3.5]()
+        %13 : int = prim::Constant[value=1]()
+        %23 : int = prim::Constant[value=127]()
+        %4 : int[] = prim::ListConstruct(%3)
+        %11 : Tensor = aten::full(%4, %5, %6, %9, %35, %9)
+        %12 : int[] = prim::ListConstruct(%3)
+        %19 : Tensor = aten::full(%12, %13, %7, %9, %35, %9)
+        %quant_input.1 : Tensor = aten::fake_quantize_per_tensor_affine(%x.1, %11, %19, %22, %23)
+        return (%quant_input.1))IR";
+
+  auto g = std::make_shared<torch::jit::Graph>();
+  torch::jit::parseIR(graph, g.get());
+
+  auto in = at::randint(1, 10, {1, 5, 5, 5}, {at::kCUDA}).to(at::kFloat);
+
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in});
+
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in}, nvinfer1::DataType::kINT8);
+
+  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0], 2e-6));
+}
+
 TEST(Converters, ATenFakeQuantizePerChannelConvertsCorrectly) {
   const auto graph = R"IR(
     graph(%x.1 : Tensor):