diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index a556b2a4..d6b76a5b 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -22568,7 +22568,17 @@ static int ggml_compute_forward(struct ggml_compute_params * params, struct ggml
             } break;
         case GGML_OP_ARGSORT:
             {
-                ggml_compute_forward_argsort(params, tensor);
+                if (i + 5 < cgraph->n_nodes &&
+                    cgraph->nodes[i+1]->op == GGML_OP_VIEW &&
+                    cgraph->nodes[i+2]->op == GGML_OP_GET_ROWS &&
+                    cgraph->nodes[i+3]->op == GGML_OP_RESHAPE &&
+                    cgraph->nodes[i+4]->op == GGML_OP_SOFT_MAX &&
+                    cgraph->nodes[i+5]->op == GGML_OP_RESHAPE) {
+                    iqk_openai_experts(tensor, cgraph->nodes[i+4], params->ith, params->nth);
+                    i += 5;
+                } else {
+                    ggml_compute_forward_argsort(params, tensor);
+                }
             } break;
         case GGML_OP_ARGSORT_THRESH:
             {
diff --git a/ggml/src/iqk/iqk_cpu_ops.cpp b/ggml/src/iqk/iqk_cpu_ops.cpp
index 51cdfcc8..ff34abcf 100644
--- a/ggml/src/iqk/iqk_cpu_ops.cpp
+++ b/ggml/src/iqk/iqk_cpu_ops.cpp
@@ -345,3 +345,47 @@ void iqk_glm45moe_experts(struct ggml_tensor * dst, struct ggml_tensor * topk_vi
         }
     }
 }
+
+void iqk_openai_experts(struct ggml_tensor * topk, struct ggml_tensor * softmax, int ith, int nth) {
+
+    auto probs    = topk->src[0];
+
+    auto nrows = ggml_nrows(probs);
+    auto npt   = (nrows + nth - 1)/nth;
+    auto first = npt*ith;
+    auto last  = std::min(first + npt, nrows);
+    if (last <= first) return;
+
+    int ne00 = probs->ne[0];
+    int ne0  = softmax->ne[0];
+    GGML_ASSERT(ggml_is_contiguous(probs));
+    GGML_ASSERT(ggml_is_contiguous(softmax));
+    GGML_ASSERT(ne0 <= ne00);
+    //if (ith == 0) printf("%s: ne00 = %d, ne0 = %d, topk: %s, softmax: %s\n", __func__, ne00, ne0, ggml_type_name(topk->type), ggml_type_name(softmax->type));
+    //if (ith == 0) printf("%s: ne00 = %d, ne0 = %d, topk: %s, %ld x %ld x %ld x %ld, %zu x %zu x %zu x %zu\n", __func__, ne00, ne0, ggml_type_name(topk->type), topk->ne[0], topk->ne[1], topk->ne[2], topk->ne[3], topk->nb[0], topk->nb[1], topk->nb[2], topk->nb[3]);
+
+    size_t work_size = ne00;
+    auto& aux = get_work_buffer(work_size);
+
+    for (int ir = first; ir < last; ++ir) {
+        auto data = (const float *)((const char *)probs->data + ir*probs->nb[1]);
+        for (int j = 0; j < ne00; ++j) aux[j] = { data[j], j };
+        if (ne0 < ne00) {
+            std::partial_sort(aux.begin(), aux.begin() + ne0, aux.begin() + ne00, std::greater<std::pair<float,int>>{});
+        } else {
+            std::sort(aux.begin(), aux.begin() + ne00, std::greater<std::pair<float,int>>{});
+        }
+        auto weights = (float *)((char *)softmax->data + ir*softmax->nb[1]);
+        auto ids = (int32_t *)((char *)topk->data + ir*topk->nb[1]);
+        float max = aux.front().first;
+        float sum = 0;
+        for (int j = 0; j < ne0; ++j) {
+            weights[j] = expf(aux[j].first - max);
+            ids[j]     = aux[j].second;
+            sum += weights[j];
+        }
+        GGML_ASSERT(sum > 0);
+        float norm = 1/sum;
+        for (int j = 0; j < ne0; ++j) weights[j] *= norm;
+    }
+}
diff --git a/ggml/src/iqk/iqk_cpu_ops.h b/ggml/src/iqk/iqk_cpu_ops.h
index 2de3a5cb..ef2bbe1a 100644
--- a/ggml/src/iqk/iqk_cpu_ops.h
+++ b/ggml/src/iqk/iqk_cpu_ops.h
@@ -22,6 +22,8 @@ void iqk_bailingmoev2_experts(struct ggml_tensor * dst, struct ggml_tensor * top
 
 void iqk_glm45moe_experts(struct ggml_tensor * dst, struct ggml_tensor * topk_view, int ith, int nth);
 
+void iqk_openai_experts(struct ggml_tensor * topk, struct ggml_tensor * softmax, int ith, int nth);
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/src/llama-build-context.cpp b/src/llama-build-context.cpp
index e2fccfca..dd0b62be 100644
--- a/src/llama-build-context.cpp
+++ b/src/llama-build-context.cpp
@@ -836,10 +836,6 @@ llm_expert_gating_func_type   gating_op,
             ggml_reshape_3d(ctx, probs, 1, n_expert, n_tokens), selected_experts); // [1, n_expert_used, n_tokens]
     cb(weights, "ffn_moe_weights", il);
 
-    if (graph) {
-        ggml_build_forward_expand(graph, weights);
-    }
-
     if (gating_op == LLM_EXPERT_GATING_FUNC_TYPE_SOFTMAX_WEIGHT) {
         weights = ggml_reshape_2d(ctx, weights, n_expert_used, n_tokens);
         weights = ggml_soft_max(ctx, weights); // [n_expert_used, n_tokens]
@@ -847,6 +843,10 @@ llm_expert_gating_func_type   gating_op,
         cb(weights, "ffn_moe_weights_softmax", il);
     }
 
+    if (graph) {
+        ggml_build_forward_expand(graph, weights);
+    }
+
     if (norm_w) {
         weights = ggml_reshape_2d(ctx, weights, n_expert_used, n_tokens);