diff --git a/src/graphs/build_qwen35.cpp b/src/graphs/build_qwen35.cpp
index 9ffc66e0..f0ac83e8 100644
--- a/src/graphs/build_qwen35.cpp
+++ b/src/graphs/build_qwen35.cpp
@@ -291,14 +291,10 @@ struct ggml_tensor * llm_build_context::build_qwen35_mtp(
     const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
 
     cur = build_std_attention(gf, mtp_layer.attn_norm, cur,
-            inp_pos, nullptr, nullptr,
+            inp_pos, inp_out_ids, nullptr,
             KQ_mask, nullptr, nullptr,
             kq_scale, 0.0f, 0, il, true, false, true, false, true, nullptr);
 
-    if (inp_out_ids) {
-        cur = ggml_get_rows(ctx0, cur, inp_out_ids);
-    }
-
     // Dense FFN — optional (9B and 4B don't have FFN in MTP layer)
     if (mtp_layer.ffn_gate != nullptr) {
         cur = llm_build_ffn(ctx0, lctx, mtp_layer.ffn_norm, cur,
@@ -312,11 +308,6 @@ struct ggml_tensor * llm_build_context::build_qwen35_mtp(
     cur = lctx.cvec.apply_to(ctx0, cur, il);
     cb(cur, "ffn_out", il);
 
-    // As far as I can tell this was wrong. We need the FFN output, and not the normalized result.
-    //cur = llm_build_norm(ctx0, cur, hparams, mtp_layer.nextn.shared_head_norm, NULL, LLM_NORM_RMS, cb, il);
-    cb(cur, "result_norm", -1);
-
-    //cur = build_output(lctx, ctx0, cur, model.output, nullptr, cb);
     cur = build_output(lctx, ctx0, cur, model.output_mtp, mtp_layer.nextn.shared_head_norm, cb);
     cb(cur, "result_output", -1);
 
diff --git a/src/llama-load-tensors.cpp b/src/llama-load-tensors.cpp
index 60bdd666..a1300cce 100644
--- a/src/llama-load-tensors.cpp
+++ b/src/llama-load-tensors.cpp
@@ -179,10 +179,10 @@ struct create_tensors_helper : public create_tensors_helper_interface {
     inline ggml_context * ctx_for_layer(int i) const {
         return ctx_map.at(model.buft_layer[i].buft);
     }
-    inline ggml_context * ctx_for_layer_split(int i) const {
+    inline ggml_context * ctx_for_layer_split(int i, bool force_split = false) const {
         const bool is_mtp_layer = model.hparams.nextn_predict_layers > 0 &&
                                   static_cast<uint32_t>(i) >= model.hparams.n_layer - model.hparams.nextn_predict_layers;
-        return is_mtp_layer ? ctx_map.at(model.buft_layer[i].buft) : ctx_map.at(model.buft_layer[i].buft_matrix);
+        return is_mtp_layer && !force_split ? ctx_map.at(model.buft_layer[i].buft) : ctx_map.at(model.buft_layer[i].buft_matrix);
     }
 
     std::map<ggml_backend_buffer_type_t, int> buft_layer_count;
@@ -1733,8 +1733,7 @@ bool create_tensors_helper::create_qwen35_tensors(const LLM_TN & tn) {
         const bool is_mtp_layer = hparams.nextn_predict_layers > 0 &&
                                   static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers;
 
-        // For now only run MTP into the per-layer
-        ggml_context * ctx_split = is_mtp_layer ? ctx_for_layer(i) : ctx_for_layer_split(i);
+        ggml_context * ctx_split = ctx_for_layer_split(i, true);
 
         int flags = 0;
         // Skip loading MTP layers if the feature is disabled
@@ -4609,6 +4608,7 @@ bool create_tensors_helper::create_tensors() {
         for (int il = 0; il < n_layer; ++il) {
             // For now only run MTP into the per-layer
             if (model.mtp && hparams.nextn_predict_layers > 0 &&
+                model.arch != LLM_ARCH_QWEN35 &&
                 static_cast<uint32_t>(il) >= static_cast<uint32_t>(n_layer) - hparams.nextn_predict_layers) {
                 LLAMA_LOG_DEBUG("%s: not splitting MTP tail layer %d (forced non-split)\n", __func__, il);
                 continue;
diff --git a/src/llama.cpp b/src/llama.cpp
index 308f296f..245509e2 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -880,7 +880,7 @@ static bool llama_kv_cache_init(
     std::map<ggml_backend_buffer_type_t, int> buft_layer_count;
     if (offload) {
         const bool is_mtp = (model.arch == LLM_ARCH_GLM_DSA ||
-                             model.arch == LLM_ARCH_QWEN35 ||
+                             //model.arch == LLM_ARCH_QWEN35 ||
                              model.arch == LLM_ARCH_QWEN35MOE) && hparams.nextn_predict_layers > 0;
         const int64_t n_mtp_first = hparams.n_layer - hparams.nextn_predict_layers;
         for (int64_t i = 0; i < n_layer; ++i) {
@@ -960,10 +960,10 @@ static bool llama_kv_cache_init(
 
     int n_mla = 0;
     int n_kv_active_layers = 0;
-    const int64_t n_mtp_first_layer = hparams.n_layer - hparams.nextn_predict_layers;
+    const int n_mtp_first_layer = hparams.n_layer - hparams.nextn_predict_layers;
     for (int i = 0; i < (int) n_layer; i++) {
         // For MTP-only context, skip KV allocation for non-MTP layers
-        if (cparams.mtp_op_type != MTP_OP_NONE && i < (int)n_mtp_first_layer) {
+        if (cparams.mtp_op_type != MTP_OP_NONE && i < n_mtp_first_layer) {
             cache.k_l.push_back(nullptr);
             if (!is_mla_attn || !cparams.mla_attn || (cparams.mla_attn == 1 && !cparams.flash_attn)) {
                 cache.v_l.push_back(nullptr);
@@ -976,10 +976,10 @@ static bool llama_kv_cache_init(
         const uint32_t n_head_kv    = hparams.n_head_kv(i);
         const uint32_t n_embd_head_k= hparams.n_embd_head_k(i);
 
-        const bool is_mtp_tail_layer = (model.arch == LLM_ARCH_QWEN35 ||
+        const bool is_mtp_tail_layer = (//model.arch == LLM_ARCH_QWEN35 ||
                                         model.arch == LLM_ARCH_QWEN35MOE ||
                                         model.arch == LLM_ARCH_GLM_DSA) &&
-                hparams.nextn_predict_layers > 0 && i >= (int)n_mtp_first_layer;
+                hparams.nextn_predict_layers > 0 && i >= n_mtp_first_layer;
         //struct ggml_context * ctx = split_cache && !qnext_recurrent ? ctx_map.at(model.buft_layer[i].buft_matrix) : offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front();
         struct ggml_context * ctx = ((split_cache || replicate_mla) && !is_mtp_tail_layer) ? ctx_map.at(model.buft_layer[i].buft_matrix) : offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front();
         ggml_tensor * k = nullptr;