diff --git a/src/llama-build-context.cpp b/src/llama-build-context.cpp
index 4ef58e4b..cae676ea 100644
--- a/src/llama-build-context.cpp
+++ b/src/llama-build-context.cpp
@@ -1827,9 +1827,9 @@ std::tuple<ggml_tensor*, ggml_tensor*, ggml_tensor*, ggml_tensor*> llm_build_con
         cb(Kcur, "Kcur_normed", il);
         ggml_build_forward_expand(gf, Kcur);
     }
-    gate = ggml_sigmoid(ctx0, gate);
+    //gate = ggml_sigmoid(ctx0, gate);
     //gate = ggml_reshape_2d(ctx0, gate, gate->ne[0]*gate->ne[1], gate->ne[2]);
-    cb(gate, "gate", il);
+    //cb(gate, "gate", il);
     return {Qcur, Kcur, Vcur, gate};
 }
 
@@ -4536,62 +4536,6 @@ ggml_cgraph * llm_build_context::build_qwen35moe() {
     const int64_t n_embd_head = hparams.n_embd_head_v;
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
 
-    int sections[4];
-    std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
-
-    auto build_layer_attn = [&](ggml_tensor * cur, ggml_tensor * inp_pos, ggml_tensor * KQ_mask, int il) -> ggml_tensor * {
-
-        auto Qaux = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
-        auto Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
-        auto Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
-        cb(Qaux, "Qaux", il);
-        cb(Kcur, "Kcur", il);
-        cb(Vcur, "Vcur", il);
-        ggml_build_forward_expand(gf, Qaux);
-        ggml_build_forward_expand(gf, Kcur);
-        ggml_build_forward_expand(gf, Vcur);
-
-        Qaux = ggml_reshape_3d(ctx0, Qaux, n_embd_head * 2, n_head, n_tokens);
-        auto Qcur = ggml_cont(ctx0, ggml_view_3d(ctx0, Qaux, n_embd_head, n_head, n_tokens, Qaux->nb[1], Qaux->nb[2], 0));
-        auto gate = ggml_cont_2d(ctx0, ggml_view_3d(ctx0, Qaux, n_embd_head, n_head, n_tokens, Qaux->nb[1], Qaux->nb[2], n_embd_head*ggml_element_size(Qaux)), n_embd_head*n_head, n_tokens);
-        cb(Qcur, "Qcur", il);
-        cb(gate, "gate", il);
-
-        Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
-        Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-        Qcur = llm_build_norm(ctx0, Qcur, hparams, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, cb, il);
-        cb(Qcur, "Qcur_normed", il);
-
-        Kcur = llm_build_norm(ctx0, Kcur, hparams, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, cb, il);
-        cb(Kcur, "Kcur_normed", il);
-
-        Qcur = ggml_rope_multi(ctx0, Qcur, inp_pos, nullptr,
-            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
-
-        Kcur = ggml_rope_multi(ctx0, Kcur, inp_pos, nullptr,
-            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
-
-        cb(Qcur, "Qcur_roped", il);
-        cb(Kcur, "Kcur_roped", il);
-
-        ggml_tensor * attn = llm_build_kv(ctx0, lctx, kv_self, gf, nullptr, nullptr,
-                Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv,
-                hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale, cb, il);
-        cb(attn, "attn_pregate", il);
-
-        gate = ggml_sigmoid(ctx0, gate);
-        cb(gate, "gate_sigmoid", il);
-        attn = ggml_mul(ctx0, attn, gate);
-        cb(attn, "attn_gated", il);
-
-        attn = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo, attn);
-        cb(attn, "attn_output", il);
-
-        return attn;
-
-    };
-
     ggml_tensor * inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
     ggml_tensor * inp_pos = build_inp_pos();
     ggml_tensor * inp_out_ids = n_tokens > 1 ? build_inp_out_ids() : nullptr;
@@ -4601,6 +4545,8 @@ ggml_cgraph * llm_build_context::build_qwen35moe() {
     cb(lctx.inp_s_seq_qnext, "inp_s_seq_qnext", -1);
     ggml_set_input(lctx.inp_s_seq_qnext);
 
+    float KQ_scale = hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+
     ggml_tensor * causal_mask = nullptr;
     ggml_tensor * identity    = nullptr;
     ggml_tensor * diag_mask   = nullptr;
@@ -4616,25 +4562,26 @@ ggml_cgraph * llm_build_context::build_qwen35moe() {
     ggml_tensor * cur = nullptr;
 
     for (int il = 0; il < n_layer; ++il) {
-        ggml_tensor * inpSA = inpL;
-
-        cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, cb, il);
-        cb(cur, "attn_norm", il);
 
         if (hparams.is_recurrent(il)) {
+            ggml_tensor * inpSA = inpL;
+
+            cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
             cur = delta.build_layer_attn_linear(ctx0, gf, cur, causal_mask, identity, diag_mask, il, cb);
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            cur = ggml_add(ctx0, cur, inpSA);
+            cb(cur, "attn_residual", il);
         } else {
-            cur = build_layer_attn(cur, inp_pos, KQ_mask, il);
+            cur = build_std_attention(gf, model.layers[il].attn_norm, inpL, inp_pos, il == n_layer - 1 ? inp_out_ids : nullptr, nullptr,
+                    KQ_mask, nullptr, nullptr, KQ_scale, 0.0f, 0, il, true, false, true, false, true);
         }
 
-        if (il == n_layer - 1 && inp_out_ids) {
-            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
-            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-        }
-
-        cur = ggml_add(ctx0, cur, inpSA);
-        cb(cur, "attn_residual", il);
-
         cur = llm_build_std_moe_ffn(ctx0, lctx, model.layers[il].ffn_norm, cur,
                 model.layers[il].ffn_gate_inp,  nullptr,
                 model.layers[il].ffn_up_exps,   nullptr,
@@ -4673,62 +4620,6 @@ ggml_cgraph * llm_build_context::build_qwen35() {
     const int64_t n_embd_head = hparams.n_embd_head_v;
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
 
-    int sections[4];
-    std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
-
-    auto build_layer_attn = [&](ggml_tensor * cur, ggml_tensor * inp_pos, ggml_tensor * KQ_mask, int il) -> ggml_tensor * {
-
-        auto Qaux = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
-        auto Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
-        auto Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
-        cb(Qaux, "Qaux", il);
-        cb(Kcur, "Kcur", il);
-        cb(Vcur, "Vcur", il);
-        ggml_build_forward_expand(gf, Qaux);
-        ggml_build_forward_expand(gf, Kcur);
-        ggml_build_forward_expand(gf, Vcur);
-
-        Qaux = ggml_reshape_3d(ctx0, Qaux, n_embd_head * 2, n_head, n_tokens);
-        auto Qcur = ggml_cont(ctx0, ggml_view_3d(ctx0, Qaux, n_embd_head, n_head, n_tokens, Qaux->nb[1], Qaux->nb[2], 0));
-        auto gate = ggml_cont_2d(ctx0, ggml_view_3d(ctx0, Qaux, n_embd_head, n_head, n_tokens, Qaux->nb[1], Qaux->nb[2], n_embd_head*ggml_element_size(Qaux)), n_embd_head*n_head, n_tokens);
-        cb(Qcur, "Qcur", il);
-        cb(gate, "gate", il);
-
-        Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
-        Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-        Qcur = llm_build_norm(ctx0, Qcur, hparams, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, cb, il);
-        cb(Qcur, "Qcur_normed", il);
-
-        Kcur = llm_build_norm(ctx0, Kcur, hparams, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, cb, il);
-        cb(Kcur, "Kcur_normed", il);
-
-        Qcur = ggml_rope_multi(ctx0, Qcur, inp_pos, nullptr,
-            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
-
-        Kcur = ggml_rope_multi(ctx0, Kcur, inp_pos, nullptr,
-            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
-
-        cb(Qcur, "Qcur_roped", il);
-        cb(Kcur, "Kcur_roped", il);
-
-        ggml_tensor * attn = llm_build_kv(ctx0, lctx, kv_self, gf, nullptr, nullptr,
-                Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv,
-                hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale, cb, il);
-        cb(attn, "attn_pregate", il);
-
-        gate = ggml_sigmoid(ctx0, gate);
-        cb(gate, "gate_sigmoid", il);
-        attn = ggml_mul(ctx0, attn, gate);
-        cb(attn, "attn_gated", il);
-
-        attn = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo, attn);
-        cb(attn, "attn_output", il);
-
-        return attn;
-
-    };
-
     ggml_tensor * inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
     ggml_tensor * inp_pos = build_inp_pos();
     ggml_tensor * inp_out_ids = n_tokens > 1 ? build_inp_out_ids() : nullptr;
@@ -4738,6 +4629,8 @@ ggml_cgraph * llm_build_context::build_qwen35() {
     cb(lctx.inp_s_seq_qnext, "inp_s_seq_qnext", -1);
     ggml_set_input(lctx.inp_s_seq_qnext);
 
+    float KQ_scale = hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+
     ggml_tensor * causal_mask = nullptr;
     ggml_tensor * identity    = nullptr;
     ggml_tensor * diag_mask   = nullptr;
@@ -4753,25 +4646,23 @@ ggml_cgraph * llm_build_context::build_qwen35() {
     ggml_tensor * cur = nullptr;
 
     for (int il = 0; il < n_layer; ++il) {
-        ggml_tensor * inpSA = inpL;
-
-        cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, cb, il);
-        cb(cur, "attn_norm", il);
 
         if (hparams.is_recurrent(il)) {
+            ggml_tensor * inpSA = inpL;
+            cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
             cur = delta.build_layer_attn_linear(ctx0, gf, cur, causal_mask, identity, diag_mask, il, cb);
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            cur = ggml_add(ctx0, cur, inpSA);
+            cb(cur, "attn_residual", il);
         } else {
-            cur = build_layer_attn(cur, inp_pos, KQ_mask, il);
+            cur = build_std_attention(gf, model.layers[il].attn_norm, inpL, inp_pos, il == n_layer - 1 ? inp_out_ids : nullptr, nullptr,
+                    KQ_mask, nullptr, nullptr, KQ_scale, 0.0f, 0, il, true, false, true, false, true);
         }
 
-        if (il == n_layer - 1 && inp_out_ids) {
-            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
-            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-        }
-
-        cur = ggml_add(ctx0, cur, inpSA);
-        cb(cur, "attn_residual", il);
-
         cur = llm_build_ffn(ctx0, lctx, model.layers[il].ffn_norm, cur,
                 model.layers[il].ffn_up,   NULL, NULL,
                 model.layers[il].ffn_gate, NULL, NULL,
@@ -10254,7 +10145,7 @@ ggml_tensor * llm_build_context::build_std_attention(ggml_cgraph * gf, ggml_tens
                 auto the_k_norm = model.layers[il].attn_k_norm ? model.layers[il].attn_k_norm->extra ?
                     ((ggml_split_tensor_t *)model.layers[il].attn_k_norm->extra)->splits[id] : model.layers[il].attn_k_norm : nullptr;
                 ggml_tensor *Qcur, *Kcur, *Vcur, *gate = nullptr;
-                if (model.arch == LLM_ARCH_QWEN3NEXT) {
+                if (model.arch == LLM_ARCH_QWEN3NEXT || model.arch == LLM_ARCH_QWEN35 || model.arch == LLM_ARCH_QWEN35MOE) {
                     auto [Q, K, V, G] = llm_build_mul_mat_qkv_gated(gf, cur, split_wq, split_wk, split_wv,
                             the_q_norm, the_k_norm, il);
                     Qcur = Q; Kcur = K; Vcur = V; gate = G;
@@ -10393,7 +10284,13 @@ ggml_tensor * llm_build_context::build_std_attention(ggml_cgraph * gf, ggml_tens
                 cur = ggml_reshape_2d(ctx0, cur, split_wo->ne[0], n_tokens);
                 cb(cur, "flash_attn_reshaped", il_cb);
                 if (gate) {
-                    cur = ggml_mul(ctx0, cur, gate);
+                    if (false && cur->ne[1] == 1) { // we need to add GGML_UNARY_OP_SIGMOID to the ops supported by ggml_fused_mul_unary
+                        cur = ggml_fused_mul_unary(ctx0, cur, gate, GGML_UNARY_OP_SIGMOID);
+                    } else {
+                        gate = ggml_sigmoid(ctx0, gate);
+                        cb(gate, "gate", il_cb);
+                        cur = ggml_mul(ctx0, cur, gate);
+                    }
                     cb(cur, "qkv_gated", il_cb);
                 }
 
@@ -10445,7 +10342,7 @@ ggml_tensor * llm_build_context::build_std_attention(ggml_cgraph * gf, ggml_tens
     auto input_normed = cur;
 
     ggml_tensor *Qcur, *Kcur, *Vcur, *gate = nullptr;
-    if (model.arch == LLM_ARCH_QWEN3NEXT) {
+    if (model.arch == LLM_ARCH_QWEN3NEXT || model.arch == LLM_ARCH_QWEN35 || model.arch == LLM_ARCH_QWEN35MOE) {
         auto [Q, K, V, G] = llm_build_mul_mat_qkv_gated(gf, cur, model.layers[il].wq, model.layers[il].wk, model.layers[il].wv,
                 model.layers[il].attn_q_norm, model.layers[il].attn_k_norm, il);
         Qcur = Q; Kcur = K; Vcur = V; gate = G;
@@ -10506,7 +10403,13 @@ ggml_tensor * llm_build_context::build_std_attention(ggml_cgraph * gf, ggml_tens
         if (gate) {
             cur = llm_build_kv(ctx0, lctx, kv_self, gf, nullptr, nullptr,
                     Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, KQ_scale, cb, il, sinks, n_swa);
-            cur = ggml_mul(ctx0, cur, gate);
+            if (false && cur->ne[1] == 1) { // we need to add GGML_UNARY_OP_SIGMOID to the ops supported by ggml_fused_mul_unary
+                cur = ggml_fused_mul_unary(ctx0, cur, gate, GGML_UNARY_OP_SIGMOID);
+            } else {
+                gate = ggml_sigmoid(ctx0, gate);
+                cb(gate, "gate", il);
+                cur = ggml_mul(ctx0, cur, gate);
+            }
             cb(cur, "qkv_gated", il);
             cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo, cur);
             if (model.layers[il].bo) {