diff --git a/codeflash/api/aiservice.py b/codeflash/api/aiservice.py
index 4ef96b308..96e467072 100644
--- a/codeflash/api/aiservice.py
+++ b/codeflash/api/aiservice.py
@@ -224,7 +224,7 @@ def get_jit_rewritten_code(  # noqa: D417
         logger.info("!lsp|Rewriting as a JIT function…")
         console.rule()
         try:
-            response = self.make_ai_service_request("/rewrite_jit", payload=payload, timeout=60)
+            response = self.make_ai_service_request("/rewrite_jit", payload=payload, timeout=self.timeout)
         except requests.exceptions.RequestException as e:
             logger.exception(f"Error generating jit rewritten candidate: {e}")
             ph("cli-jit-rewrite-error-caught", {"error": str(e)})
diff --git a/codeflash/cli_cmds/cli.py b/codeflash/cli_cmds/cli.py
index df0ae57ce..e135cd022 100644
--- a/codeflash/cli_cmds/cli.py
+++ b/codeflash/cli_cmds/cli.py
@@ -84,6 +84,9 @@ def parse_args() -> Namespace:
     parser.add_argument(
         "--no-gen-tests", action="store_true", help="Do not generate tests, use only existing tests for optimization."
     )
+    parser.add_argument(
+        "--no-jit-opts", action="store_true", help="Do not generate JIT-compiled optimizations for numerical code."
+    )
     parser.add_argument("--staging-review", action="store_true", help="Upload optimizations to staging for review")
     parser.add_argument(
         "--verify-setup",
diff --git a/codeflash/optimization/function_optimizer.py b/codeflash/optimization/function_optimizer.py
index 697061b9c..256b65b7a 100644
--- a/codeflash/optimization/function_optimizer.py
+++ b/codeflash/optimization/function_optimizer.py
@@ -610,7 +610,9 @@ def optimize_function(self) -> Result[BestOptimization, str]:
         ):
             console.rule()
             new_code_context = code_context
-            if self.is_numerical_code:  # if the code is numerical in nature (uses numpy/tensorflow/math/pytorch/jax)
+            if (
+                self.is_numerical_code and not self.args.no_jit_opts
+            ):  # if the code is numerical in nature (uses numpy/tensorflow/math/pytorch/jax)
                 jit_compiled_opt_candidate = self.aiservice_client.get_jit_rewritten_code(
                     code_context.read_writable_code.markdown, self.function_trace_id
                 )
@@ -639,7 +641,7 @@ def optimize_function(self) -> Result[BestOptimization, str]:
                 read_writable_code=code_context.read_writable_code,
                 read_only_context_code=code_context.read_only_context_code,
                 run_experiment=should_run_experiment,
-                is_numerical_code=self.is_numerical_code,
+                is_numerical_code=self.is_numerical_code and not self.args.no_jit_opts,
             )
 
             concurrent.futures.wait([future_tests, future_optimizations])
@@ -1158,7 +1160,7 @@ def determine_best_candidate(
             )
             if self.experiment_id
             else None,
-            is_numerical_code=self.is_numerical_code,
+            is_numerical_code=self.is_numerical_code and not self.args.no_jit_opts,
         )
 
         processor = CandidateProcessor(