diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index e679b24e87..ec91b7c6d0 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1223,6 +1223,8 @@ void llama_model_base::load_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_POOLING_TYPE,            hparams.pooling_type,    false);
     ml.get_key(LLM_KV_BLOCK_COUNT,             hparams.n_layer_all);
     GGML_ASSERT(hparams.n_layer_all > 0 && hparams.n_layer_all <= LLAMA_MAX_LAYERS);
+    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS,    hparams.n_layer_nextn,   false);
+    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all);
     ml.get_key(LLM_KV_EXPERT_COUNT,            hparams.n_expert,        false);
     ml.get_key(LLM_KV_EXPERT_USED_COUNT,       hparams.n_expert_used,   false);
     ml.get_key(LLM_KV_EXPERT_GROUP_COUNT,      hparams.n_expert_groups, false);
diff --git a/src/models/bailingmoe2.cpp b/src/models/bailingmoe2.cpp
index 5000e9c6db..8fc0ea752b 100644
--- a/src/models/bailingmoe2.cpp
+++ b/src/models/bailingmoe2.cpp
@@ -9,9 +9,6 @@ void llama_model_bailingmoe2::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,              hparams.expert_weights_scale, false);
     ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,               hparams.expert_weights_norm, false);
     ml.get_key(LLM_KV_EXPERT_GATING_FUNC,                hparams.expert_gating_func);
-    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS,              hparams.n_layer_nextn, false);
-
-    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_impl");
 
     switch (hparams.n_layer()) {
         case 20: type = LLM_TYPE_16B_A1B; break;
diff --git a/src/models/bailingmoe3.cpp b/src/models/bailingmoe3.cpp
index 0637931cc0..1583d9be34 100644
--- a/src/models/bailingmoe3.cpp
+++ b/src/models/bailingmoe3.cpp
@@ -22,7 +22,6 @@ void llama_model_bailingmoe3::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,             hparams.expert_weights_scale, false);
     ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,              hparams.expert_weights_norm, false);
     ml.get_key(LLM_KV_EXPERT_GATING_FUNC,               hparams.expert_gating_func);
-    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS,             hparams.n_layer_nextn, false);
     ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP,           hparams.swiglu_clamp_exp,   hparams.n_layer_all, false);
     ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_SHEXP,         hparams.swiglu_clamp_shexp, hparams.n_layer_all, false);
 
diff --git a/src/models/cohere2moe.cpp b/src/models/cohere2moe.cpp
index 3acb7e77af..c50910edc5 100644
--- a/src/models/cohere2moe.cpp
+++ b/src/models/cohere2moe.cpp
@@ -20,9 +20,6 @@ void llama_model_cohere2moe::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);
     ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func, false);
 
-    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS,        hparams.n_layer_nextn, false);
-    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer");
-
     if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
         hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
     }
diff --git a/src/models/deepseek2.cpp b/src/models/deepseek2.cpp
index e0e537e005..3a76187aa5 100644
--- a/src/models/deepseek2.cpp
+++ b/src/models/deepseek2.cpp
@@ -37,11 +37,6 @@ void llama_model_deepseek2::load_arch_hparams(llama_model_loader & ml) {
         hparams.rope_yarn_log_mul /= 0.1f;
     }
 
-    // NextN/MTP
-    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
-    GGML_ASSERT(hparams.n_layer_nextn == 0 ||
-        hparams.n_layer() + hparams.n_layer_nextn == hparams.n_layer_all);
-
     // (optional) temperature tuning - used by mistral-large
     ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE,  hparams.f_attn_temp_scale,       false);
     ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_LENGTH, hparams.n_attn_temp_floor_scale, false); // FIXME why not use temperature_length?
diff --git a/src/models/deepseek32.cpp b/src/models/deepseek32.cpp
index 2b82a780c4..079bdfc302 100644
--- a/src/models/deepseek32.cpp
+++ b/src/models/deepseek32.cpp
@@ -37,10 +37,6 @@ void llama_model_deepseek32::load_arch_hparams(llama_model_loader & ml) {
         hparams.rope_yarn_log_mul /= 0.1f;
     }
 
-    // NextN/MTP parameters
-    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
-    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer");
-
     switch (hparams.n_layer()) {
         case 61: type = LLM_TYPE_685B_A37B; break;
         default: type = LLM_TYPE_UNKNOWN;
diff --git a/src/models/deepseek4.cpp b/src/models/deepseek4.cpp
index fc816e2aeb..0157ce7051 100644
--- a/src/models/deepseek4.cpp
+++ b/src/models/deepseek4.cpp
@@ -17,15 +17,13 @@ static float dsv4_rope_attn_factor(float freq_scale, float ext_factor) {
 }
 
 void llama_model_deepseek4::load_arch_hparams(llama_model_loader & ml) {
-    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
-    if (hparams.n_layer_nextn > 0 && hparams.n_layer_nextn < hparams.n_layer_all) {
+    if (hparams.n_layer_nextn > 0) {
         const uint32_t n_layer_main = hparams.n_layer_all - hparams.n_layer_nextn;
         const std::string mtp_probe = "blk." + std::to_string(n_layer_main) + ".nextn.eh_proj.weight";
         if (ml.get_weight(mtp_probe.c_str()) == nullptr) {
             hparams.n_layer_nextn = 0;
         }
     }
-    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < block_count");
 
     ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
     ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK,       hparams.n_lora_q);
diff --git a/src/models/dots3note.cpp b/src/models/dots3note.cpp
index 00a008c2c9..7656562b05 100644
--- a/src/models/dots3note.cpp
+++ b/src/models/dots3note.cpp
@@ -9,10 +9,6 @@ void llama_model_dots3note::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
     hparams.f_norm_eps = 1e-6;  // eps for the indexer k_norm layer norm
 
-    // TODO: use MTP layer
-    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
-    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_all");
-
     // MoE parameters
     ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,        hparams.n_expert_shared);
     ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
diff --git a/src/models/exaone-moe.cpp b/src/models/exaone-moe.cpp
index 5aed937940..86e5a3a984 100644
--- a/src/models/exaone-moe.cpp
+++ b/src/models/exaone-moe.cpp
@@ -20,9 +20,6 @@ void llama_model_exaone_moe::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,               hparams.expert_weights_norm, false);
     ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,         hparams.n_layer_dense_lead, false);
 
-    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
-    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_impl");
-
     switch (hparams.n_layer()) {
         case 32: type = LLM_TYPE_30B_A3B; break;
         case 48: type = LLM_TYPE_235B_A22B; break;
diff --git a/src/models/exaone4.cpp b/src/models/exaone4.cpp
index a06819a67c..9ba978956d 100644
--- a/src/models/exaone4.cpp
+++ b/src/models/exaone4.cpp
@@ -1,9 +1,6 @@
 #include "models.h"
 
 void llama_model_exaone4::load_arch_hparams(llama_model_loader & ml) {
-    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
-    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer");
-
     if (hparams.n_layer() == 64) {    // 32B
         hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
         hparams.n_swa = 4096;
diff --git a/src/models/gemma4-assistant.cpp b/src/models/gemma4-assistant.cpp
index 6378130e79..989fa42c85 100644
--- a/src/models/gemma4-assistant.cpp
+++ b/src/models/gemma4-assistant.cpp
@@ -11,9 +11,6 @@ void llama_model_gemma4_assistant::load_arch_hparams(llama_model_loader & ml) {
 
     hparams.f_attention_scale = 1.0f;
 
-    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
-    GGML_ASSERT(hparams.n_layer_nextn == hparams.n_layer_all && "n_layer_nextn must be == n_layer_impl");
-
     ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA,           hparams.rope_freq_base_train_swa, false);
     ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,     hparams.n_swa);
     ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,  hparams.f_norm_rms_eps);
diff --git a/src/models/glm-dsa.cpp b/src/models/glm-dsa.cpp
index 93a1448b46..543b15cf35 100644
--- a/src/models/glm-dsa.cpp
+++ b/src/models/glm-dsa.cpp
@@ -56,10 +56,6 @@ void llama_model_glm_dsa::load_arch_hparams(llama_model_loader & ml) {
         hparams.expert_gating_func =  LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
     }
 
-    // NextN/MTP parameters
-    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
-    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_all");
-
     // BC for GLM 5, 5.1 (full indexers) without indexer_types metadata
     const bool is_pre_5_2 = hparams.n_ctx_train < 1048576;
     if (is_pre_5_2) {
@@ -70,9 +66,7 @@ void llama_model_glm_dsa::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key_or_arr(LLM_KV_ATTENTION_INDEXER_TYPES, hparams.is_indexer_full_impl, hparams.n_layer(), false);
 
     switch (hparams.n_layer()) {
-        case 78: // GGUF with NextN/MTP metadata: n_layer() excludes the nextn layer
-        case 79:
-            type = LLM_TYPE_744B_A40B; break;
+        case 78: type = LLM_TYPE_744B_A40B; break;
         default: type = LLM_TYPE_UNKNOWN;
     }
 }
diff --git a/src/models/glm4-moe.cpp b/src/models/glm4-moe.cpp
index 83ea7f8ac6..1d2ac65fd7 100644
--- a/src/models/glm4-moe.cpp
+++ b/src/models/glm4-moe.cpp
@@ -17,10 +17,6 @@ void llama_model_glm4_moe::load_arch_hparams(llama_model_loader & ml) {
         hparams.expert_gating_func =  LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
     }
 
-    // NextN/MTP parameters
-    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
-    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_impl");
-
     switch (hparams.n_layer()) {
         case 46: type = LLM_TYPE_106B_A12B; break; // GLM-4.5-Air
         case 48: type = LLM_TYPE_102B_A12B; break; // Solar Open
diff --git a/src/models/glm4.cpp b/src/models/glm4.cpp
index b4326c5f21..463be809d8 100644
--- a/src/models/glm4.cpp
+++ b/src/models/glm4.cpp
@@ -4,10 +4,6 @@ void llama_model_glm4::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,    hparams.f_norm_rms_eps);
     ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
 
-    // NextN/MTP parameters (GLM-OCR)
-    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
-    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_impl");
-
     switch (hparams.n_layer()) {
         case 17: type = LLM_TYPE_1B; break; // GLM-OCR
         case 40: type = LLM_TYPE_9B; break;
diff --git a/src/models/hy-v3.cpp b/src/models/hy-v3.cpp
index 61db93af85..3c45331b11 100644
--- a/src/models/hy-v3.cpp
+++ b/src/models/hy-v3.cpp
@@ -13,10 +13,6 @@ void llama_model_hy_v3::load_arch_hparams(llama_model_loader & ml) {
         hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
     }
 
-    // NextN/MTP (HY V3): extra decoder block(s) appended beyond the main stack
-    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
-    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_all");
-
     switch (hparams.n_layer()) {
         case 48: type = LLM_TYPE_30B_A3B; break;
         default: type = LLM_TYPE_UNKNOWN;
diff --git a/src/models/mimo2.cpp b/src/models/mimo2.cpp
index d50e186cce..1dc554220d 100644
--- a/src/models/mimo2.cpp
+++ b/src/models/mimo2.cpp
@@ -16,9 +16,6 @@ void llama_model_mimo2::load_arch_hparams(llama_model_loader & ml) {
         hparams.f_attn_value_scale = value_scale;
     }
 
-    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
-    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_impl");
-
     switch (hparams.n_layer()) {
         case 48: type = LLM_TYPE_310B_A15B; break;
         default: type = LLM_TYPE_UNKNOWN;
diff --git a/src/models/nemotron-h.cpp b/src/models/nemotron-h.cpp
index f02674c646..55640c996a 100644
--- a/src/models/nemotron-h.cpp
+++ b/src/models/nemotron-h.cpp
@@ -7,10 +7,6 @@ void llama_model_nemotron_h::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
     ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
 
-    // NextN/MTP: optional draft head appended as extra trailing block(s)
-    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
-    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_all");
-
     // A layer is recurrent IFF the n_head_kv value is set to 0 and
     // the n_ff value is set to 0. Appended MTP blocks are dense (non-recurrent)
     for (uint32_t i = 0; i < hparams.n_layer_all; ++i) {
diff --git a/src/models/qwen35.cpp b/src/models/qwen35.cpp
index 309dd43244..0b9210981d 100644
--- a/src/models/qwen35.cpp
+++ b/src/models/qwen35.cpp
@@ -12,10 +12,6 @@ void llama_model_qwen35::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
     ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
 
-    // NextN/MTP (Qwen3.5/3.6): extra decoder block appended beyond the main stack
-    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
-    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_impl");
-
     // Mark recurrent layers (linear attention layers). MTP layers are dense
     // attention-only and must be flagged non-recurrent.
     if (!ml.get_key_or_arr(LLM_KV_ATTENTION_RECURRENT_LAYERS, hparams.is_recr_impl, hparams.n_layer_all, false)) {
diff --git a/src/models/qwen35moe.cpp b/src/models/qwen35moe.cpp
index 38f2a57985..9bf4ea4328 100644
--- a/src/models/qwen35moe.cpp
+++ b/src/models/qwen35moe.cpp
@@ -15,10 +15,6 @@ void llama_model_qwen35moe::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
     ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
 
-    // NextN/MTP (Qwen3.5/3.6): extra decoder block appended beyond the main stack
-    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
-    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_impl");
-
     // Mark recurrent layers (linear attention layers). MTP layers are dense
     // attention-only and must be flagged non-recurrent.
     if (!ml.get_key_or_arr(LLM_KV_ATTENTION_RECURRENT_LAYERS, hparams.is_recr_impl, hparams.n_layer_all, false)) {
diff --git a/src/models/qwen3next.cpp b/src/models/qwen3next.cpp
index 0808fd87aa..b2b8809c7f 100644
--- a/src/models/qwen3next.cpp
+++ b/src/models/qwen3next.cpp
@@ -13,10 +13,6 @@ void llama_model_qwen3next::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
     ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
 
-    // NextN/MTP: extra decoder block appended beyond the main stack
-    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
-    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_all");
-
     // Mark recurrent layers (linear attention layers).
     if (!ml.get_key_or_arr(LLM_KV_ATTENTION_RECURRENT_LAYERS, hparams.is_recr_impl, hparams.n_layer_all, false)) {
         uint32_t full_attn_interval = 4;
diff --git a/src/models/step35.cpp b/src/models/step35.cpp
index 5b1d902581..d101d115ef 100644
--- a/src/models/step35.cpp
+++ b/src/models/step35.cpp
@@ -23,14 +23,10 @@ void llama_model_step35::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,  hparams.n_swa);
     ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA,        hparams.rope_freq_base_train_swa, false);
 
-    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, hparams.n_layer());
+    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, hparams.n_layer_all);
 
-    ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP,   hparams.swiglu_clamp_exp,   hparams.n_layer(), false);
-    ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_SHEXP, hparams.swiglu_clamp_shexp, hparams.n_layer(), false);
-
-    // NextN/MTP (Step3p5): extra decoder block appended beyond the main stack.
-    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
-    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_impl");
+    ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP,   hparams.swiglu_clamp_exp,   hparams.n_layer_all, false);
+    ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_SHEXP, hparams.swiglu_clamp_shexp, hparams.n_layer_all, false);
 
     switch (hparams.n_layer()) {
         case 45: type = LLM_TYPE_196B_A11B; break;