diff --git a/python/sglang/srt/layers/torchao_utils.py b/python/sglang/srt/layers/torchao_utils.py
index e2d557fb7..c5bca25df 100644
--- a/python/sglang/srt/layers/torchao_utils.py
+++ b/python/sglang/srt/layers/torchao_utils.py
@@ -11,6 +11,22 @@ import torch
 logger = logging.getLogger(__name__)
 
 
+def get_gemlite_cache_path() -> str:
+    return f"/tmp/{pwd.getpwuid(os.getuid()).pw_gecos}_gemlite.json"
+
+
+def save_gemlite_cache(print_error: bool = False) -> bool:
+    try:
+        from gemlite.core import GemLiteLinearTriton
+
+        GemLiteLinearTriton.cache_config(get_gemlite_cache_path())
+    except Exception:
+        if print_error:
+            logger.error("Failed to save the GemLite cache.")
+        return False
+    return True
+
+
 def apply_torchao_config_to_model(
     model: torch.nn.Module, torchao_config: str, filter_fn=None
 ):
@@ -74,9 +90,7 @@ def apply_torchao_config_to_model(
         )
 
         # try to load gemlite kernel config
-        GemLiteLinearTriton.load_config(
-            f"/tmp/{pwd.getpwuid(os.getuid()).pw_gecos}_gemlite.json"
-        )
+        GemLiteLinearTriton.load_config(get_gemlite_cache_path())
 
     elif "fp8wo" in torchao_config:
         # this requires newer hardware
diff --git a/python/sglang/srt/model_executor/cuda_graph_runner.py b/python/sglang/srt/model_executor/cuda_graph_runner.py
index a9c2c3781..3e9030742 100644
--- a/python/sglang/srt/model_executor/cuda_graph_runner.py
+++ b/python/sglang/srt/model_executor/cuda_graph_runner.py
@@ -31,6 +31,7 @@ from sglang.srt.layers.logits_processor import (
     LogitsProcessorOutput,
 )
 from sglang.srt.layers.moe.fused_moe_native import fused_moe_forward_native
+from sglang.srt.layers.torchao_utils import save_gemlite_cache
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode
 from sglang.srt.utils import maybe_torch_compile, monkey_patch_vllm_all_gather
 
@@ -276,6 +277,9 @@ class CudaGraphRunner:
                     self.graphs[bs] = graph
                     self.output_buffers[bs] = output_buffers
 
+                # Save gemlite cache after each capture
+                save_gemlite_cache()
+
     def capture_one_batch_size(self, bs: int, forward: Callable):
         graph = torch.cuda.CUDAGraph()
         stream = self.stream