diff --git a/onnxruntime/contrib_ops/cpu/quantization/qlinear_concat.cc b/onnxruntime/contrib_ops/cpu/quantization/qlinear_concat.cc
index da4c65375a..024f64a652 100644
--- a/onnxruntime/contrib_ops/cpu/quantization/qlinear_concat.cc
+++ b/onnxruntime/contrib_ops/cpu/quantization/qlinear_concat.cc
@@ -32,8 +32,8 @@ static inline bool has_same_zero_point(bool is_signed, const Tensor* tensor_x_ze
 
 QLinearConcat::QLinearConcat(const OpKernelInfo& info) : OpKernel(info), ConcatBase(info) {
   size_t input_def_count = info.node().InputDefs().size();
-  ORT_ENFORCE(input_def_count >= 8 && (input_def_count - 2) % 3 == 0,
-              "At least two inputs are needed, and each input must be (tensor, scale, zero_point) tuple!");
+  ORT_ENFORCE(input_def_count >= 5 && (input_def_count - 2) % 3 == 0,
+              "Each input must be (tensor, scale, zero_point) tuple!");
 
   size_t input_count = (input_def_count - 2) / 3;
   fixed_lookup_tables_.resize(input_count);
@@ -90,8 +90,8 @@ Status QLinearConcat::Compute(OpKernelContext* ctx) const {
 
   // Number of input tensors to concatenate (tupled)
   auto input_count_x3 = Node().InputArgCount()[2];
-  ORT_ENFORCE(input_count_x3 >= 6 && input_count_x3 % 3 == 0,
-              "At least two inputs are needed, and each input must be (tensor, scale, zero_point) tuple!");
+  ORT_ENFORCE(input_count_x3 >= 3 && input_count_x3 % 3 == 0,
+              "Each input must be (tensor, scale, zero_point) tuple!");
 
   // Hold pointers to the input tensors to be used in the PrepareForCompute() step
   auto input_count = input_count_x3 / 3;
diff --git a/onnxruntime/core/graph/contrib_ops/quantization_defs.cc b/onnxruntime/core/graph/contrib_ops/quantization_defs.cc
index 74dbcd86d4..7e9995aff5 100644
--- a/onnxruntime/core/graph/contrib_ops/quantization_defs.cc
+++ b/onnxruntime/core/graph/contrib_ops/quantization_defs.cc
@@ -785,7 +785,7 @@ Wwhere the function `Sigmoid(x) = 1 / (1 + exp(-x))` )DOC";
       .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
         propagateElemTypeFromInputToOutput(ctx, 0, 0);
         auto numInputs = ctx.getNumInputs();
-        if (numInputs < 8 || (numInputs - 2) % 3 != 0 ||
+        if (numInputs < 5 || (numInputs - 2) % 3 != 0 ||
             !hasNInputShapes(ctx, static_cast<int>(numInputs))) {
           return;
         }
diff --git a/onnxruntime/test/contrib_ops/qlinear_concat_test.cc b/onnxruntime/test/contrib_ops/qlinear_concat_test.cc
index ea59d17adc..506d16d6b1 100644
--- a/onnxruntime/test/contrib_ops/qlinear_concat_test.cc
+++ b/onnxruntime/test/contrib_ops/qlinear_concat_test.cc
@@ -213,5 +213,31 @@ TEST(QLinearConcatS8, ExpectFail_WrongZeroPointType_1) {
   QLinearConcat3InputsS8({true, true, true}, FailByWrongZeroPointType);
 }
 
+void QLinearConcatOneInputS8(std::vector<bool> is_const_inputs, QLinearConcatFailCause fail_by = NoFail) {
+  std::vector<int64_t> y_shape = {2, 1, 3};
+  std::vector<std::vector<int64_t>> x_shapes = {{2, 1, 3}};
+  std::vector<std::vector<int8_t>> x_vecs = { {0, -2, 3, -5, 127, -128} };
+  std::vector<int8_t> x_zero_points = {0};
+  std::vector<float> x_scales = {1.0};
+
+  float y_scale = 0.25;
+  int8_t y_zero_point = 0;
+  std::vector<int8_t> y_vec = { 0, -8, 12, -20, 127, -128 };
+
+  RunQLinearConcat<int8_t>(x_shapes, x_vecs, 1, x_scales, x_zero_points, y_shape, y_vec, y_scale, y_zero_point,
+                           is_const_inputs, fail_by);
+
+  RunQLinearConcat<int8_t>(x_shapes, x_vecs, -2, x_scales, x_zero_points, y_shape, y_vec, y_scale, y_zero_point,
+                           is_const_inputs, fail_by);
+}
+
+TEST(QLinearConcatS8, InputOne_Dynamic) {
+  QLinearConcatOneInputS8({false});
+}
+
+TEST(QLinearConcatS8, InputOne_Const) {
+  QLinearConcatOneInputS8({true});
+}
+
 }  // namespace test
 }  // namespace onnxruntime