diff --git a/src/backends/torch/torchlib.cc b/src/backends/torch/torchlib.cc
index b579da89e..ea5c13d56 100644
--- a/src/backends/torch/torchlib.cc
+++ b/src/backends/torch/torchlib.cc
@@ -30,6 +30,7 @@
 #include <sys/stat.h>
 #include <sys/types.h>
 #include <fcntl.h>
+#include <torch/csrc/jit/runtime/graph_executor.h>
 
 #include "native/native.h"
 #include "torchsolver.h"
@@ -188,9 +189,20 @@ namespace dd
   void TorchLib<TInputConnectorStrategy, TOutputConnectorStrategy,
                 TMLModel>::init_mllib(const APIData &lib_ad)
   {
-    // Get parameters
     auto mllib_dto = lib_ad.createSharedDTO<DTO::MLLib>();
 
+    // experimental: jit compiler parameters
+    if (mllib_dto->jit_compiler_params)
+      {
+        auto jit_params = mllib_dto->jit_compiler_params;
+        torch::jit::FusionStrategy strat = {
+          { torch::jit::FusionBehavior::STATIC, jit_params->fusion_static },
+          { torch::jit::FusionBehavior::DYNAMIC, jit_params->fusion_dynamic }
+        };
+        auto old_strat = torch::jit::setFusionStrategy(strat);
+      }
+
+    // publish model (from existing repository)
     if (mllib_dto->from_repository != nullptr)
       {
         this->_mlmodel.copy_to_target(mllib_dto->from_repository,
diff --git a/src/dto/mllib.hpp b/src/dto/mllib.hpp
index b6c118525..a1b70d741 100644
--- a/src/dto/mllib.hpp
+++ b/src/dto/mllib.hpp
@@ -45,6 +45,38 @@ namespace dd
       DTO_FIELD(Int32, test_batch_size) = 1;
     };
 
+    // Torch JIT compiler
+    /** This parameters are set using the function setFusionStrategy:
+     * https://github.com/pytorch/pytorch/blob/4c074a9b8bd2e6d8940b40a41ce399e6c4a463a9/torch/csrc/jit/runtime/profiling_graph_executor_impl.cpp#L131
+     */
+    class JitCompilerParameters : public oatpp::DTO
+    {
+      DTO_INIT(JitCompilerParameters, DTO)
+
+      DTO_FIELD_INFO(fusion_static)
+      {
+        info->description
+            = "Set number of STATIC specializations that can occur when "
+              "compiling jit models. In STATIC fusion, fused ops are compiled "
+              "to have fixed shapes. See "
+              "https://pytorch.org/docs/stable/generated/"
+              "torch.jit.set_fusion_strategy.html for more details.";
+      }
+      DTO_FIELD(Int32, fusion_static) = 2;
+
+      DTO_FIELD_INFO(fusion_dynamic)
+      {
+        info->description
+            = "Set number of DYNAMIC specializations that can occur when "
+              "compiling jit models. In DYNAMIC fusion, fused ops are "
+              "compiled to have variable input shape, so that this "
+              "specialization can be used with multiple shapes. See "
+              "https://pytorch.org/docs/stable/generated/"
+              "torch.jit.set_fusion_strategy.html for more details";
+      }
+      DTO_FIELD(Int32, fusion_dynamic) = 10;
+    };
+
     class MLLib : public oatpp::DTO
     {
       DTO_INIT(MLLib, DTO /* extends */)
@@ -191,6 +223,16 @@ namespace dd
       }
       DTO_FIELD(Boolean, concurrent_predict) = true;
 
+      DTO_FIELD_INFO(jit_compiler_params)
+      {
+        info->description
+            = "[experimental] Modify torch jit compiler parameters. This can "
+              "be useful when the model takes too long to compile, for "
+              "example. Beware, this parameter applies instantly for all dede "
+              "services.";
+      }
+      DTO_FIELD(Object<JitCompilerParameters>, jit_compiler_params);
+
       // Libtorch predict options
       DTO_FIELD_INFO(forward_method)
       {