diff --git a/onnxruntime/core/codegen/mti/math/unary_ops.cc b/onnxruntime/core/codegen/mti/math/unary_ops.cc
index a9b1807298..ae732ea33e 100644
--- a/onnxruntime/core/codegen/mti/math/unary_ops.cc
+++ b/onnxruntime/core/codegen/mti/math/unary_ops.cc
@@ -30,7 +30,7 @@ tvm::Tensor Ceil(const tvm::Tensor& X, const std::string& name) {
   return topi::ceil(X, name);
 }
 
-tvm::Tensor Clip(const tvm::Tensor& X, float min_value, float max_value, const std::string& name) {
+tvm::Tensor Clip(const tvm::Tensor& X, tvm::Expr min_value, tvm::Expr max_value, const std::string& name) {
   auto Y = tvm::compute(
       X->shape,
       [&](const tvm::Array<tvm::Var>& indices) {
diff --git a/onnxruntime/core/codegen/mti/math/unary_ops.h b/onnxruntime/core/codegen/mti/math/unary_ops.h
index ae1f17099f..aeb336262e 100644
--- a/onnxruntime/core/codegen/mti/math/unary_ops.h
+++ b/onnxruntime/core/codegen/mti/math/unary_ops.h
@@ -11,7 +11,7 @@ namespace tvm_codegen {
 tvm::Tensor Abs(const tvm::Tensor& X, const std::string& name = "abs");
 tvm::Tensor Affine(const tvm::Tensor& X, float alpha, float beta, const std::string& name = "affine");
 tvm::Tensor Ceil(const tvm::Tensor& X, const std::string& name = "ceil");
-tvm::Tensor Clip(const tvm::Tensor& X, float min_value, float max_value, const std::string& name = "clip");
+tvm::Tensor Clip(const tvm::Tensor& X, tvm::Expr min_value, tvm::Expr max_value, const std::string& name = "clip");
 tvm::Tensor Elu(const tvm::Tensor& X, float alpha, const std::string& name = "elu");
 tvm::Tensor Exp(const tvm::Tensor& X, const std::string& name = "exp");
 tvm::Tensor Floor(const tvm::Tensor& X, const std::string& name = "floor");
diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/math/clip.cc b/onnxruntime/core/codegen/passes/op_ir_creator/math/clip.cc
index 88383624f8..1a905f6104 100644
--- a/onnxruntime/core/codegen/passes/op_ir_creator/math/clip.cc
+++ b/onnxruntime/core/codegen/passes/op_ir_creator/math/clip.cc
@@ -13,14 +13,31 @@ namespace tvm_codegen {
 Status GENERIC_OP_IR_CREATOR_CLASS(Clip)::Evaluate(
     const tvm::Array<tvm::Tensor>& inputs,
     const Node& node,
-    CodeGenContext&,
+    CodeGenContext& ctx_codegen,
     tvm::Array<tvm::Tensor>& outputs) {
   ProtoHelperNodeContext ctx(node);
   OpNodeProtoHelper<ProtoHelperNodeContext> info(&ctx);
 
-  float max_value, min_value;
-  ORT_RETURN_IF_ERROR(info.GetAttr<float>("max", &max_value));
-  ORT_RETURN_IF_ERROR(info.GetAttr<float>("min", &min_value));
+  int version = ctx_codegen.GetCodeGenHandle()->domain_version_lookup_func(node.Domain());
+  tvm::Expr min_value, max_value;
+  if (version < 11) {
+    float max_v, min_v;
+    info.GetAttrOrDefault("min", &min_v, std::numeric_limits<float>::lowest());
+    info.GetAttrOrDefault("max", &max_v, std::numeric_limits<float>::max());
+    min_value = tvm::make_const(tvm::Float(32), min_v);
+    max_value = tvm::make_const(tvm::Float(32), max_v);
+  } else {
+    // for op_version >= 11, max and min are optional inputs
+    min_value = tvm::make_const(tvm::Float(32), std::numeric_limits<float>::lowest());
+    max_value = tvm::make_const(tvm::Float(32), std::numeric_limits<float>::max());
+    auto num_inputs = inputs.size();
+    if (num_inputs >= 2) {
+      min_value = inputs[1]();
+    }
+    if (num_inputs == 3) {
+      max_value = inputs[2]();
+    }
+  }
 
   tvm::Tensor Y = Clip(inputs[0], min_value, max_value, node.Name() + "_Clip");
   outputs.push_back(Y);
diff --git a/onnxruntime/core/providers/nuphar/kernel.h b/onnxruntime/core/providers/nuphar/kernel.h
index b6f6833e3a..53bc29eb32 100644
--- a/onnxruntime/core/providers/nuphar/kernel.h
+++ b/onnxruntime/core/providers/nuphar/kernel.h
@@ -83,7 +83,8 @@ class NupharKernelState {
   NUPHAR_OP(AveragePool, 10, DataTypeImpl::AllIEEEFloatTensorExceptHalfTypes())             \
   NUPHAR_OP(AveragePool, 11, DataTypeImpl::AllIEEEFloatTensorExceptHalfTypes())             \
   NUPHAR_OP(Ceil, 6, DataTypeImpl::AllIEEEFloatTensorTypes())                               \
-  NUPHAR_OP(Clip, 6, DataTypeImpl::AllIEEEFloatTensorTypes())                               \
+  NUPHAR_VERSIONED_OP(Clip, 6, 10, DataTypeImpl::AllIEEEFloatTensorTypes())                 \
+  NUPHAR_VERSIONED_OP(Clip, 11, 11, DataTypeImpl::AllIEEEFloatTensorTypes())                \
   NUPHAR_VERSIONED_OP(Concat, 4, 10, DataTypeImpl::AllFixedSizeTensorTypes())               \
   NUPHAR_OP(Concat, 11, DataTypeImpl::AllFixedSizeTensorTypes())                            \
   DISABLE_MACRO(NUPHAR_OP(Conv, 1, DataTypeImpl::AllIEEEFloatTensorExceptHalfTypes()))      \
diff --git a/onnxruntime/core/providers/nuphar/partition/partitioner.cc b/onnxruntime/core/providers/nuphar/partition/partitioner.cc
index 45ac6a39cc..462ceaa523 100644
--- a/onnxruntime/core/providers/nuphar/partition/partitioner.cc
+++ b/onnxruntime/core/providers/nuphar/partition/partitioner.cc
@@ -256,6 +256,7 @@ Status Partitioner::Evaluate(const onnxruntime::GraphViewer& graph, bool disting
     if (IsNodeSupported(*node)) {
       AcceptNode(graph, node_idx);
     } else {
+      LOGS_DEFAULT(INFO) << "unsupported node (" << node->Name() << ") in nuphar provider";
       RejectNode(graph, node_idx);
     }
   }