diff --git a/include/tvm/relax/transform.h b/include/tvm/relax/transform.h
index 8c6417d19d14..f743bb53d089 100644
--- a/include/tvm/relax/transform.h
+++ b/include/tvm/relax/transform.h
@@ -281,6 +281,12 @@ TVM_DLL Pass UpdateVDevice(VDevice new_vdevice, int64_t index);
  */
 TVM_DLL Pass ExpandTupleArguments();
 
+/*! \brief Remove unused parameters to internal functions
+ *
+ * \return The Pass
+ */
+TVM_DLL Pass RemoveUnusedParameters();
+
 /*! \brief Remove unused outputs from internal functions
  *
  * \return The Pass
diff --git a/python/tvm/relax/transform/__init__.py b/python/tvm/relax/transform/__init__.py
index b6887160c8b5..c3f037da5f64 100644
--- a/python/tvm/relax/transform/__init__.py
+++ b/python/tvm/relax/transform/__init__.py
@@ -56,6 +56,7 @@
     PatternCheckContext,
     RealizeVDevice,
     RemovePurityChecking,
+    RemoveUnusedParameters,
     RemoveUnusedOutputs,
     RewriteCUDAGraph,
     RewriteDataflowReshape,
diff --git a/python/tvm/relax/transform/transform.py b/python/tvm/relax/transform/transform.py
index 1beb535f0bb7..0af89b7d9e8d 100644
--- a/python/tvm/relax/transform/transform.py
+++ b/python/tvm/relax/transform/transform.py
@@ -568,6 +568,16 @@ def ExpandTupleArguments() -> tvm.ir.transform.Pass:
     return _ffi_api.ExpandTupleArguments()  # type: ignore
 
 
+def RemoveUnusedParameters() -> tvm.ir.transform.Pass:
+    """Remove unused arguments to internal functions
+
+    Returns
+    -------
+    ret: tvm.ir.transform.Pass
+    """
+    return _ffi_api.RemoveUnusedParameters()  # type: ignore
+
+
 def RemoveUnusedOutputs() -> tvm.ir.transform.Pass:
     """Remove unused outputs from internal functions
 
diff --git a/src/relax/transform/remove_unused_parameters.cc b/src/relax/transform/remove_unused_parameters.cc
new file mode 100644
index 000000000000..d053d56f3205
--- /dev/null
+++ b/src/relax/transform/remove_unused_parameters.cc
@@ -0,0 +1,260 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+#include <tvm/relax/analysis.h>
+#include <tvm/relax/expr_functor.h>
+#include <tvm/relax/transform.h>
+#include <tvm/relax/utils.h>
+
+#include <algorithm>
+#include <optional>
+#include <tuple>
+
+#include "utils.h"
+
+namespace tvm {
+namespace relax {
+
+namespace {
+
+template <typename T>
+using PSet = std::unordered_set<T, ObjectPtrHash, ObjectPtrEqual>;
+
+template <typename T, typename U>
+using PMap = std::unordered_map<T, U, ObjectPtrHash, ObjectPtrEqual>;
+
+/* \brief Describes the modifications to be made for a function */
+struct CalleeAnalysis {
+  /* \brief The updated private function */
+  Function func;
+
+  /* \brief A function that updates the callsite arguments
+   *
+   * \param The arguments used to call the original function
+   *
+   * \return The arguments to be used for the modified function
+   */
+  std::function<Array<Expr>(Array<Expr>)> arg_updater;
+};
+
+std::optional<CalleeAnalysis> AnalyzeCallee(Function func) {
+  bool is_exposed = func->attrs.GetAttr<String>(tvm::attr::kGlobalSymbol).defined();
+  if (is_exposed) return std::nullopt;
+
+  auto free_relax_vars = [&]() -> PSet<Var> {
+    auto array_free_vars = FreeVars(func->body);
+    return {array_free_vars.begin(), array_free_vars.end()};
+  }();
+
+  std::vector<bool> parameter_mask;
+  parameter_mask.reserve(func->params.size());
+
+  Array<Var> params;
+  for (const auto& param : func->params) {
+    bool is_used = free_relax_vars.count(param);
+    parameter_mask.push_back(is_used);
+    if (is_used) {
+      params.push_back(param);
+    }
+  }
+
+  if (func->params.size() == params.size()) {
+    // Early bail-out for the common case where the function uses all
+    // of its parameters.
+    return std::nullopt;
+  }
+
+  // Even if a parameter is unused, it may provide definitions for
+  // symbolic variables.  We still want to remove the relax variable
+  // to reduce computational steps in the parent, but we need to
+  // provide the symbolic variables the other steps.
+  auto defined_tir_params = [&]() -> PSet<tir::Var> {
+    auto param_sinfo =
+        TupleStructInfo(params.Map([](const auto& var) { return GetStructInfo(var); }));
+    auto arr = DefinableTIRVarsInStructInfo(param_sinfo);
+    return {arr.begin(), arr.end()};
+  }();
+
+  // Use an array to define the order of the symbolic variables
+  Array<tir::Var> free_tir_vars;
+  for (const auto& tir_var : FreeSymbolicVars(func->body)) {
+    if (!defined_tir_params.count(tir_var)) {
+      free_tir_vars.push_back(tir_var);
+    }
+  }
+
+  for (const auto& tir_var : free_tir_vars) {
+    Var relax_var("param_" + tir_var->name_hint, PrimStructInfo(tir_var));
+    params.push_back(relax_var);
+  }
+
+  FuncStructInfo new_sinfo(params.Map([](const auto& var) { return GetStructInfo(var); }),
+                           func->ret_struct_info,
+                           Downcast<FuncStructInfo>(func->struct_info_)->purity);
+
+  auto arg_updater = [parameter_mask, old_relax_params = func->params,
+                      free_tir_vars](Array<Expr> old_args) -> Array<Expr> {
+    ICHECK_EQ(old_args.size(), parameter_mask.size())
+        << "Call provides " << old_args.size() << ", but the callee accepts "
+        << parameter_mask.size() << " parameters";
+
+    Array<Expr> new_args;
+    for (size_t i = 0; i < old_args.size(); i++) {
+      if (parameter_mask.at(i)) {
+        new_args.push_back(old_args[i]);
+      }
+    }
+
+    if (free_tir_vars.size()) {
+      Map<Var, Expr> old_binding;
+      for (size_t i = 0; i < old_relax_params.size(); i++) {
+        old_binding.Set(old_relax_params[i], old_args[i]);
+      }
+      arith::Analyzer analyzer;
+      auto tir_binding = InferSymbolicVarMap(old_binding, &analyzer);
+
+      for (const auto& tir_var : free_tir_vars) {
+        new_args.push_back(PrimValue(tir_binding.at(tir_var)));
+      }
+    }
+
+    return new_args;
+  };
+
+  auto write_ptr = func.CopyOnWrite();
+  write_ptr->params = params;
+  write_ptr->struct_info_ = new_sinfo;
+
+  return CalleeAnalysis{func, arg_updater};
+}
+
+class CallSiteMutator : public ExprMutator {
+ public:
+  explicit CallSiteMutator(PMap<GlobalVar, std::function<Call(Call)>> callsite_updaters)
+      : callsite_updaters_(callsite_updaters) {}
+
+  using ExprMutator::VisitExpr_;
+
+  Expr VisitExpr_(const FunctionNode* op) override {
+    auto node = ExprMutator::VisitExpr_(op);
+
+    // If a function was modified, that means it called into a private
+    // function that now takes a reduced number of arguments.  Some
+    // bindings in the calling scope, previously used to define those
+    // unused arguments, may be able to be removed as a result.
+    if (node.get() != op) {
+      node = RemoveAllUnused(node);
+    }
+    return node;
+  }
+
+  Expr VisitExpr_(const CallNode* op) override {
+    auto node = Downcast<Call>(ExprMutator::VisitExpr_(op));
+
+    if (auto gvar = node->op.as<GlobalVar>()) {
+      if (auto it = callsite_updaters_.find(gvar.value()); it != callsite_updaters_.end()) {
+        node = it->second(std::move(node));
+      }
+    }
+
+    return node;
+  }
+
+  PMap<GlobalVar, std::function<Call(Call)>> callsite_updaters_;
+};
+
+}  // namespace
+
+namespace transform {
+
+Pass RemoveUnusedParameters() {
+  runtime::TypedPackedFunc<IRModule(IRModule, PassContext)> pass_func =
+      [=](IRModule mod, PassContext pc) -> IRModule {
+    PMap<GlobalVar, std::function<Call(Call)>> callsite_updaters;
+
+    {
+      IRModule new_callees;
+
+      for (const auto& [gvar, base_func] : mod->functions) {
+        if (auto func = base_func.as<Function>()) {
+          if (auto callee_res = AnalyzeCallee(func.value())) {
+            auto new_func = callee_res->func;
+            GlobalVar new_gvar(gvar->name_hint, new_func->checked_type_);
+            new_gvar->struct_info_ = new_func->struct_info_;
+            new_callees->Add(new_gvar, new_func);
+
+            callsite_updaters[gvar] = [old_gvar = gvar, new_gvar,
+                                       arg_updater = callee_res->arg_updater](Call call) -> Call {
+              ICHECK(call->op.same_as(old_gvar)) << "InternalError: "
+                                                 << "Updater should be applied to " << old_gvar
+                                                 << ", but was applied to " << call->op;
+              auto write_ptr = call.CopyOnWrite();
+              write_ptr->op = new_gvar;
+              write_ptr->args = arg_updater(call->args);
+              return call;
+            };
+          }
+        }
+      }
+
+      if (callsite_updaters.empty()) {
+        return mod;
+      }
+      auto write_ptr = mod.CopyOnWrite();
+
+      // Remove any private subroutines that have unused parameters,
+      // then add the updated versions.  The new private functions
+      // have the same name, but require a new GlobalVar to hold the
+      // updated StructInfo.  As a result, calling `Update()` without
+      // first calling `Remove()` introduce a duplicate name and
+      // produce an error.
+      for (const auto& it : callsite_updaters) {
+        write_ptr->Remove(it.first);
+      }
+      write_ptr->Update(new_callees);
+    }
+
+    CallSiteMutator mutator(std::move(callsite_updaters));
+
+    IRModule caller_updates;
+
+    for (const auto& [gvar, base_func] : mod->functions) {
+      if (auto func = base_func.as<Function>()) {
+        auto mutated = Downcast<Function>(mutator.VisitExpr(func.value()));
+        if (!mutated.same_as(base_func)) {
+          caller_updates->Add(gvar, mutated);
+        }
+      }
+    }
+
+    if (caller_updates->functions.size()) {
+      mod.CopyOnWrite()->Update(caller_updates);
+    }
+    return mod;
+  };
+  return CreateModulePass(pass_func, 0, "RemoveUnusedParameters", {});
+}
+
+TVM_REGISTER_GLOBAL("relax.transform.RemoveUnusedParameters")
+    .set_body_typed(RemoveUnusedParameters);
+
+}  // namespace transform
+
+}  // namespace relax
+}  // namespace tvm
diff --git a/tests/python/relax/test_transform_remove_unused_parameters.py b/tests/python/relax/test_transform_remove_unused_parameters.py
new file mode 100644
index 000000000000..82c8d0bd1d29
--- /dev/null
+++ b/tests/python/relax/test_transform_remove_unused_parameters.py
@@ -0,0 +1,101 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import tvm
+import tvm.testing
+from tvm.script import ir as I, relax as R, tir as T
+
+
+class BaseCompare(tvm.testing.CompareBeforeAfter):
+    transform = tvm.relax.transform.RemoveUnusedParameters()
+
+
+class TestSimple(BaseCompare):
+    @I.ir_module
+    class Before:
+        @R.function
+        def main(A: R.Tensor, B: R.Tensor):
+            return Before.func(A, B)
+
+        @R.function(private=True)
+        def func(A: R.Tensor, B: R.Tensor) -> R.Tensor:
+            return A
+
+    @I.ir_module
+    class Expected:
+        @R.function
+        def main(A: R.Tensor, B: R.Tensor):
+            return Expected.func(A)
+
+        @R.function(private=True)
+        def func(A: R.Tensor) -> R.Tensor:
+            return A
+
+
+class TestSymbolicVariables(BaseCompare):
+    @I.ir_module
+    class Before:
+        @R.function
+        def main(A: R.Tensor(["m", "n"], "float32")) -> R.Tensor(["m", "n"], "float32"):
+            return Before.func(A)
+
+        @R.function(private=True)
+        def func(A: R.Tensor(["m", "n"], "float32")) -> R.Tensor(["m", "n"], "float32"):
+            m = T.int64()
+            n = T.int64()
+            return R.zeros(R.shape([m, n]), dtype="float32")
+
+    @I.ir_module
+    class Expected:
+        @R.function
+        def main(A: R.Tensor(["m", "n"], "float32")) -> R.Tensor(["m", "n"], "float32"):
+            m = T.int64()
+            n = T.int64()
+            out: R.Tensor([m, n], "float32") = Expected.func(R.prim_value(n), R.prim_value(m))
+            return out
+
+        @R.function(private=True)
+        def func(
+            param_n: R.Prim(value="n"), param_m: R.Prim(value="m")
+        ) -> R.Tensor(["m", "n"], "float32"):
+            m = T.int64()
+            n = T.int64()
+            return R.zeros(R.shape([m, n]), dtype="float32")
+
+
+class TestNoExtraSymbolicVariables(BaseCompare):
+    """Don't add symbolic variables if they can be inferred."""
+
+    @I.ir_module
+    class Before:
+        @R.function
+        def main(A: R.Tensor(["m", "n"], "float32")) -> R.Tensor(["m", "n"], "float32"):
+            return Before.func(A)
+
+        @R.function(private=True)
+        def func(A: R.Tensor(["m", "n"], "float32")) -> R.Tensor(["m", "n"], "float32"):
+            m = T.int64()
+            n = T.int64()
+            zeros = R.zeros(R.shape([m, n]), dtype="float32")
+            out = R.add(A, zeros)
+            return out
+
+    Expected = Before
+
+
+if __name__ == "__main__":
+    tvm.testing.main()