diff --git a/ggml-tsi-kernel b/ggml-tsi-kernel
index d99484cc626a0..61ce53b2935e5 160000
--- a/ggml-tsi-kernel
+++ b/ggml-tsi-kernel
@@ -1 +1 @@
-Subproject commit d99484cc626a0ea8c15b21092ae8db50ffacf556
+Subproject commit 61ce53b2935e56818d1a8465634eea378e7ac86d
diff --git a/ggml/include/ggml-tsavorite.h b/ggml/include/ggml-tsavorite.h
index 09cf4ac1dbd9b..eb2e27d19b3c6 100644
--- a/ggml/include/ggml-tsavorite.h
+++ b/ggml/include/ggml-tsavorite.h
@@ -129,6 +129,16 @@ enum ggml_tsavorite_kernel_type {
   GGML_TSAVORITE_KERNEL_TYPE_RMS_NORM,
   GGML_TSAVORITE_KERNEL_TYPE_SIGMOID,
   GGML_TSAVORITE_KERNEL_TYPE_SILU,
+  //Below GELU Kernel
+  GGML_TSAVORITE_KERNEL_TYPE_REGLU,
+  GGML_TSAVORITE_KERNEL_TYPE_GEGLU,
+
+  // Currently Below kernel Implemented
+  GGML_TSAVORITE_KERNEL_TYPE_SWIGLU,
+
+  GGML_TSAVORITE_KERNEL_TYPE_SWIGLU_OAI,
+  GGML_TSAVORITE_KERNEL_TYPE_GEGLU_ERF,
+  GGML_TSAVORITE_KERNEL_TYPE_GEGLU_QUICK,
 
   GGML_TSAVORITE_KERNEL_TYPE_COUNT
 };
@@ -174,6 +184,7 @@ extern void _mlir_ciface_txe_abs_host(void *a, void *res);
 extern void _mlir_ciface_txe_sin_host(void *a, void *res);
 extern void _mlir_ciface_txe_sigmoid_host(void *a, void *res);
 extern void _mlir_ciface_txe_silu_host(void *a, void *res);
+extern void _mlir_ciface_txe_swiglu_host(void *a, void *b, void *res);
 extern void _mlir_ciface_txe_rms_norm_host(void *a, void *res, void *buf);
 
 /* 
@@ -190,6 +201,7 @@ extern void _mlir_ciface_txe_abs_16_host(void *a, void *res);
 extern void _mlir_ciface_txe_sin_16_host(void *a, void *res);
 extern void _mlir_ciface_txe_sigmoid_16_host(void *a, void *res);
 extern void _mlir_ciface_txe_silu_16_host(void *a, void *res);
+extern void _mlir_ciface_txe_swiglu_16_host(void *a, void *b, void *res);
 extern void _mlir_ciface_txe_rms_norm_16_host(void *a, void *res, void *buf);
 
 extern void ggml_tsi_log_tensor_data(tensor_log log_data);
diff --git a/ggml/src/ggml-tsavorite/ggml-tsavorite.cpp b/ggml/src/ggml-tsavorite/ggml-tsavorite.cpp
index 41aa8f401951e..7e676d1e70313 100644
--- a/ggml/src/ggml-tsavorite/ggml-tsavorite.cpp
+++ b/ggml/src/ggml-tsavorite/ggml-tsavorite.cpp
@@ -477,6 +477,14 @@ static txe_compute_pipeline_state_s tsi_kernel_setup(enum ggml_tsavorite_kernel_
           kernel_pipeline->kernel_name = "TXE_RMS_NORM";
           flag = true;
           break;
+      case GGML_TSAVORITE_KERNEL_TYPE_SWIGLU:
+	  {
+          kernel_pipeline->_mlir_fptr_2_input[DATA_TYPE_F32_INDEX] = &_mlir_ciface_txe_swiglu_host;
+          kernel_pipeline->_mlir_fptr_2_input[DATA_TYPE_F16_INDEX] = &_mlir_ciface_txe_swiglu_16_host;
+          kernel_pipeline->kernel_name = "TXE_SWI_GLU";
+          flag = true;
+          break;
+	  }
       default:
           break;
   }
@@ -625,6 +633,7 @@ static struct ggml_backend_tsavorite_context *ggml_tsavorite_init(ggml_backend_d
     GGML_TSAVORITE_KERNEL(GGML_TSAVORITE_KERNEL_TYPE_SIGMOID,            true);
     GGML_TSAVORITE_KERNEL(GGML_TSAVORITE_KERNEL_TYPE_SILU,               true);
     GGML_TSAVORITE_KERNEL(GGML_TSAVORITE_KERNEL_TYPE_RMS_NORM,           true);
+    GGML_TSAVORITE_KERNEL(GGML_TSAVORITE_KERNEL_TYPE_SWIGLU,             true);
   }
 
   GGML_TSAVORITE_LOG_INFO("End %s\n", __func__);
@@ -704,7 +713,7 @@ static ggml_backend_tsavorite_buffer_s ggml_tsavorite_get_buffer(struct ggml_ten
     return tsi_nil;
 }
 #endif
-bool is_op_dtype_consistent_with_src(const struct ggml_tensor *op) {
+static bool is_op_dtype_consistent_with_src(const struct ggml_tensor *op) {
   uint32_t tensor_data_type = op->type;
   for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
     if (op->src[i] != NULL) {
@@ -720,16 +729,13 @@ static bool ggml_tsavorite_supports_op(const struct ggml_backend_tsavorite_devic
   GGML_TSAVORITE_LOG_INFO("Start %s\n", __func__);
   if (!ctx_dev)
     return false;
-  for (size_t i = 0, n = 3; i < n; ++i) {
-    if (op->src[i] != NULL && op->src[i]->type != GGML_TYPE_F32) {
-      return false;
-    }
-  }
 
-  if (op->type != GGML_TYPE_F32 || op->type != GGML_TYPE_F16)
+  if (op->type != GGML_TYPE_F32 && op->type != GGML_TYPE_F16)
     return false;
+
   if (!is_op_dtype_consistent_with_src(op))
     return false;
+
   switch (op->op) {
   case GGML_OP_NONE:
   case GGML_OP_ADD:
@@ -739,9 +745,15 @@ static bool ggml_tsavorite_supports_op(const struct ggml_backend_tsavorite_devic
   case GGML_OP_SQRT:
   case GGML_OP_SQR:
   case GGML_OP_SIN:
-    break;
   case GGML_OP_RMS_NORM:
     break;
+  case GGML_OP_GLU:
+    {
+        const ggml_glu_op op_ext = ggml_get_glu_op(op);
+        if (op_ext != GGML_GLU_OP_SWIGLU)
+            return false;
+        break;
+    }
   case GGML_OP_UNARY:
     switch (ggml_get_unary_op(op)) {
     case GGML_UNARY_OP_NEG:
@@ -815,6 +827,36 @@ static MemRefDescriptor<Rank>* create_mlir_buf(int K) {
     return header;
 }
 
+static enum ggml_tsavorite_kernel_type tsi_glu_kernel_type(struct ggml_tensor *node) {
+    const ggml_glu_op op = ggml_get_glu_op(node);
+    enum ggml_tsavorite_kernel_type kernel_type;
+
+    switch (op) {
+        case GGML_GLU_OP_REGLU:
+		kernel_type = GGML_TSAVORITE_KERNEL_TYPE_REGLU;
+            break;
+        case GGML_GLU_OP_GEGLU:
+		kernel_type = GGML_TSAVORITE_KERNEL_TYPE_GEGLU;
+            break;
+        case GGML_GLU_OP_SWIGLU:
+		kernel_type = GGML_TSAVORITE_KERNEL_TYPE_SWIGLU;
+            break;
+        case GGML_GLU_OP_SWIGLU_OAI:
+		kernel_type = GGML_TSAVORITE_KERNEL_TYPE_SWIGLU_OAI;
+            break;
+        case GGML_GLU_OP_GEGLU_ERF:
+		kernel_type = GGML_TSAVORITE_KERNEL_TYPE_GEGLU_ERF;
+            break;
+        case GGML_GLU_OP_GEGLU_QUICK:
+		kernel_type = GGML_TSAVORITE_KERNEL_TYPE_GEGLU_QUICK;
+            break;
+        default:
+		kernel_type = GGML_TSAVORITE_KERNEL_TYPE_COUNT;
+	    break;
+    }
+    return kernel_type;
+}
+
 // nodes are intermediate which has multiple src tensors & operation
 // Here we create multiple thread
 // Each Thread run the command buffer & pick Tensor and execute and get the result back base on
@@ -940,6 +982,16 @@ static enum ggml_status ggml_tsavorite_graph_compute(ggml_backend_t backend,
       kernel_type = GGML_TSAVORITE_KERNEL_TYPE_RMS_NORM;
       num_of_input_tensors = TSAVORITE_UNARY_INPUT_TENSORS;
       break;
+    case GGML_OP_GLU:
+      kernel_type = tsi_glu_kernel_type(node);
+      if (!src1)
+          src1 = src0;
+      if (kernel_type == GGML_TSAVORITE_KERNEL_TYPE_COUNT) {
+        GGML_TSAVORITE_LOG_ERROR("\n GGML_OP_GLU sub type is not correct \n");
+        return GGML_STATUS_ABORTED;
+      }
+      num_of_input_tensors = TSAVORITE_TWO_INPUT_TENSORS;
+      break;
     case GGML_OP_UNARY:
       switch (ggml_get_unary_op(node)) {
       case GGML_UNARY_OP_NEG:
@@ -1916,10 +1968,12 @@ static bool ggml_backend_tsavorite_device_supports_buft(ggml_backend_dev_t dev,
 // ggml_backend_sched_backend_id_from_cur  -> ggml_backend_offload_op ->
 static bool ggml_backend_tsavorite_device_offload_op(ggml_backend_dev_t dev,
                                                      const struct ggml_tensor *op) {
-  if (op->type != GGML_TYPE_F32 || op->type != GGML_TYPE_F16)
+  if (op->type != GGML_TYPE_F32 && op->type != GGML_TYPE_F16)
     return false;
+
   if (!is_op_dtype_consistent_with_src(op))
     return false;
+
   switch (op->op) {
   case GGML_OP_NONE:
   case GGML_OP_ADD:
@@ -1931,6 +1985,13 @@ static bool ggml_backend_tsavorite_device_offload_op(ggml_backend_dev_t dev,
   case GGML_OP_SIN:
   case GGML_OP_RMS_NORM:
     break;
+  case GGML_OP_GLU:
+    {
+        const ggml_glu_op op_ext = ggml_get_glu_op(op);
+        if (op_ext != GGML_GLU_OP_SWIGLU)
+            return false;
+        break;
+    }
   case GGML_OP_UNARY:
     switch (ggml_get_unary_op(op)) {
     case GGML_UNARY_OP_NEG:
diff --git a/tsi-pkg-build.sh b/tsi-pkg-build.sh
index 351552973dcdf..6dcd6852b8e8d 100755
--- a/tsi-pkg-build.sh
+++ b/tsi-pkg-build.sh
@@ -90,7 +90,7 @@ cat > ./${TSI_GGML_BUNDLE_INSTALL_DIR}/ggml.sh << EOL
 # Set up library paths for GCC 13.3.0 compatibility
 export LD_LIBRARY_PATH=\${LD_LIBRARY_PATH}:\$(pwd)
 
-tsi_kernels=("add" "sub" "mult" "div" "abs" "inv" "neg" "sin" "sqrt" "sqr" "sigmoid" "silu" "rms_norm" "add_16" "sub_16" "mult_16" "div_16" "abs_16" "inv_16" "neg_16" "sin_16" "sqrt_16" "sqr_16" "sigmoid_16" "silu_16" "rms_norm_16")
+tsi_kernels=("add" "sub" "mult" "div" "abs" "inv" "neg" "sin" "sqrt" "sqr" "sigmoid" "silu" "rms_norm"  "swiglu" "add_16" "sub_16" "mult_16" "div_16" "abs_16" "inv_16" "neg_16" "sin_16" "sqrt_16" "sqr_16" "sigmoid_16" "silu_16" "rms_norm_16 swiglu_16")
 
 for kernel in "\${tsi_kernels[@]}"; do
     mkdir -p ${TSI_BLOB_INSTALL_DIR}/txe_\$kernel