diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 124ea28b06..650f1c8a56 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -4440,8 +4440,11 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_EXP_PROBS_B,
         MODEL_TENSOR.LAYER_OUT_NORM,
         MODEL_TENSOR.NEXTN_EH_PROJ,
+        MODEL_TENSOR.NEXTN_EMBED_TOKENS,
         MODEL_TENSOR.NEXTN_ENORM,
         MODEL_TENSOR.NEXTN_HNORM,
+        MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD,
+        MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
     ],
     MODEL_ARCH.STEP35: [
         MODEL_TENSOR.TOKEN_EMBD,
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index a8422ff605..5e501f983b 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -2243,7 +2243,9 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                         filter = [&](uint32_t il) { return il >= hparams.n_layer(); };
                     }
 
-                    if ((arch == LLM_ARCH_STEP35 || arch == LLM_ARCH_HY_V3 || arch == LLM_ARCH_GLM_DSA) && hparams.n_layer_nextn > 0) {
+                    if ((arch == LLM_ARCH_STEP35 || arch == LLM_ARCH_HY_V3 || arch == LLM_ARCH_GLM_DSA ||
+                            arch == LLM_ARCH_MIMO2) &&
+                            hparams.n_layer_nextn > 0) {
                         if (params.ctx_type == LLAMA_CONTEXT_TYPE_MTP) {
                             filter = [&](uint32_t il) { return il >= hparams.n_layer(); };
                         } else {
diff --git a/src/models/mimo2.cpp b/src/models/mimo2.cpp
index 8898916057..4080a934cb 100644
--- a/src/models/mimo2.cpp
+++ b/src/models/mimo2.cpp
@@ -25,9 +25,13 @@ void llama_model_mimo2::load_arch_hparams(llama_model_loader & ml) {
     }
 }
 
-void llama_model_mimo2::load_arch_tensors(llama_model_loader &) {
+void llama_model_mimo2::load_arch_tensors(llama_model_loader & ml) {
     LLAMA_LOAD_LOCALS;
 
+    const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
+    const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
+    const int  mtp_flags  = trunk_only ? TENSOR_NOT_REQUIRED : 0;
+
     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
 
     // output
@@ -40,41 +44,46 @@ void llama_model_mimo2::load_arch_tensors(llama_model_loader &) {
         uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i);
         uint32_t n_head = hparams.n_head(i);
 
-        // NextN/MTP layers (the last n_nextn blocks) are preserved but disabled pending support
         const bool is_nextn = i >= n_layer;
-        const int  skip     = is_nextn ? TENSOR_SKIP : 0;
+        const int  flags    = is_nextn ? mtp_flags : 0;
 
-        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, skip);
-        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_v * n_head, n_embd }, skip);
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, flags);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_v * n_head, n_embd }, flags);
 
-        layer.attn_norm  = create_tensor(tn(LLM_TENSOR_ATTN_NORM,  "weight", i), {n_embd}, skip);
-        layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, TENSOR_NOT_REQUIRED | skip);
+        layer.attn_norm  = create_tensor(tn(LLM_TENSOR_ATTN_NORM,  "weight", i), {n_embd}, flags);
+        layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, TENSOR_NOT_REQUIRED | flags);
 
-        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, skip);
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
 
         // non-MoE branch
-        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED | skip);
-        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, TENSOR_NOT_REQUIRED | skip);
-        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED | skip);
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED | flags);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, TENSOR_NOT_REQUIRED | flags);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED | flags);
 
         // MoE branch
         int64_t n_ff_exp = hparams.n_ff_exp;
-        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED | skip);
-        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp,   n_expert}, TENSOR_NOT_REQUIRED | skip);
-        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, TENSOR_NOT_REQUIRED | skip);
-        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff_exp,   n_expert}, TENSOR_NOT_REQUIRED | skip);
-        layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED | skip);
+        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED | flags);
+        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp,   n_expert}, TENSOR_NOT_REQUIRED | flags);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, TENSOR_NOT_REQUIRED | flags);
+        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff_exp,   n_expert}, TENSOR_NOT_REQUIRED | flags);
+        layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED | flags);
 
         if (is_nextn) {
-            layer.nextn.eh_proj  = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), {2 * n_embd, n_embd}, skip);
-            layer.nextn.enorm    = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM,   "weight", i), {n_embd}, skip);
-            layer.nextn.hnorm    = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM,   "weight", i), {n_embd}, skip);
-            layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, skip);
+            layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ,          "weight", i), {2 * n_embd, n_embd}, flags);
+            layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM,            "weight", i), {n_embd}, flags);
+            layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM,            "weight", i), {n_embd}, flags);
+            layer.nextn.embed_tokens     = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS,     "weight", i), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED | flags);
+            layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED | flags);
+            layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED | flags);
+            layer.layer_out_norm         = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM,         "weight", i), {n_embd}, TENSOR_NOT_REQUIRED | flags);
         }
     }
 }
 
 std::unique_ptr<llm_graph_context> llama_model_mimo2::build_arch_graph(const llm_graph_params & params) const {
+    if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) {
+        return std::make_unique<graph_mtp>(*this, params);
+    }
     return std::make_unique<graph>(*this, params);
 }
 
@@ -89,6 +98,8 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param
     ggml_tensor * inp_out_ids = build_inp_out_ids();
 
     const float v_scale = hparams.f_attn_value_scale;
+    const bool emit_h_nextn = cparams.embeddings_nextn;
+    const bool crop_last_layer = inp_out_ids && (!emit_h_nextn || cparams.embeddings_nextn_masked);
 
     for (int il = 0; il < n_layer; ++il) {
         ggml_tensor * inpSA = inpL;
@@ -168,7 +179,7 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param
             }
         }
 
-        if (il == n_layer - 1 && inp_out_ids) {
+        if (il == n_layer - 1 && crop_last_layer) {
             cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
             inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
         }
@@ -218,6 +229,15 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param
 
     cur = inpL;
 
+    if (emit_h_nextn) {
+        cb(cur, "h_nextn", -1);
+        res->t_h_nextn = cur;
+
+        if (!cparams.embeddings_nextn_masked && inp_out_ids) {
+            cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+        }
+    }
+
     cur = build_norm(cur,
             model.output_norm, NULL,
             LLM_NORM_RMS, -1);
@@ -233,3 +253,143 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param
 
     ggml_build_forward_expand(gf, cur);
 }
+
+// Mirrors MiMo's appended NextN block: normalize and fuse token and hidden inputs, run the decoder block,
+// expose its pre-head-norm state to the next draft step, then apply the shared output norm and LM head.
+// Converted checkpoints may store that shared norm as layer_out_norm, so it remains in the fallback chain.
+llama_model_mimo2::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params)
+    : llm_graph_context(params) {
+    GGML_ASSERT(hparams.n_layer_nextn > 0 && "MIMO2 MTP requires n_layer_nextn > 0");
+
+    const int il = hparams.n_layer() + cparams.nextn_layer_offset;
+    GGML_ASSERT(cparams.nextn_layer_offset >= 0 &&
+                cparams.nextn_layer_offset < (int) hparams.n_layer_nextn &&
+                "nextn_layer_offset out of range [0, n_layer_nextn)");
+
+    const auto & layer = model.layers[il];
+    GGML_ASSERT(layer.nextn.eh_proj && "MIMO2 MTP block missing nextn.eh_proj");
+    GGML_ASSERT(layer.nextn.enorm   && "MIMO2 MTP block missing nextn.enorm");
+    GGML_ASSERT(layer.nextn.hnorm   && "MIMO2 MTP block missing nextn.hnorm");
+    GGML_ASSERT(layer.wqkv          && "MIMO2 MTP requires fused attn_qkv");
+
+    const uint32_t n_head_l    = hparams.n_head(il);
+    const uint32_t n_head_kv_l = hparams.n_head_kv(il);
+
+    const float freq_base_l  = model.get_rope_freq_base(cparams, il);
+    const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
+    const float v_scale      = hparams.f_attn_value_scale;
+
+    auto inp = std::make_unique<llm_graph_input_embd>(hparams.n_embd);
+
+    inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+    ggml_set_input(inp->tokens);
+
+    inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);
+    ggml_set_input(inp->embd);
+    ggml_set_name(inp->embd, "mtp_h_input");
+
+    ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd;
+    ggml_tensor * h_input    = inp->embd;
+    ggml_tensor * tok_embd   = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);
+    cb(tok_embd, "mtp_tok_embd", il);
+
+    res->add_input(std::move(inp));
+
+    ggml_tensor * inp_pos     = build_inp_pos();
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+    auto        * inp_attn    = build_attn_inp_kv_iswa();
+
+    ggml_tensor * h_norm = build_norm(h_input, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il);
+    cb(h_norm, "mtp_hnorm", il);
+
+    ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il);
+    cb(e_norm, "mtp_enorm", il);
+
+    ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, /*dim=*/ 0);
+    cb(concat, "mtp_concat", il);
+
+    ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s);
+    cb(cur, "mtp_eh_proj", il);
+
+    ggml_tensor * inpSA = cur;
+
+    cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
+    cb(cur, "mtp_attn_norm", il);
+
+    ggml_tensor * qkv = build_lora_mm(layer.wqkv, cur, layer.wqkv_s);
+    cb(qkv, "mtp_wqkv", il);
+
+    const size_t row_k    = ggml_row_size(qkv->type, n_embd_head_k);
+    const size_t row_v    = ggml_row_size(qkv->type, n_embd_head_v);
+    const size_t row_full = qkv->nb[1];
+    const size_t k_off    = row_k * n_head_l;
+    const size_t v_off    = k_off + row_k * n_head_kv_l;
+
+    ggml_tensor * Qcur = ggml_view_3d(ctx0, qkv, n_embd_head_k, n_head_l,    n_tokens, row_k, row_full, 0);
+    ggml_tensor * Kcur = ggml_view_3d(ctx0, qkv, n_embd_head_k, n_head_kv_l, n_tokens, row_k, row_full, k_off);
+    ggml_tensor * Vcur = ggml_view_3d(ctx0, qkv, n_embd_head_v, n_head_kv_l, n_tokens, row_v, row_full, v_off);
+
+    Qcur = ggml_rope_ext(
+        ctx0, Qcur, inp_pos, nullptr,
+        n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+        ext_factor, attn_factor, beta_fast, beta_slow);
+
+    Kcur = ggml_rope_ext(
+        ctx0, Kcur, inp_pos, nullptr,
+        n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+        ext_factor, attn_factor, beta_fast, beta_slow);
+
+    cb(Qcur, "mtp_Qcur", il);
+    cb(Kcur, "mtp_Kcur", il);
+    cb(Vcur, "mtp_Vcur", il);
+
+    cur = build_attn(inp_attn,
+            layer.wo, nullptr, layer.wo_s,
+            Qcur, Kcur, Vcur, nullptr, layer.attn_sinks, nullptr,
+            1.0f / sqrtf(float(n_embd_head_k)), il);
+    cb(cur, "mtp_attn_out", il);
+
+    if (v_scale) {
+        cur = ggml_scale(ctx0, cur, v_scale);
+        cb(cur, "mtp_attn_out_scaled", il);
+    }
+
+    ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+    cb(ffn_inp, "mtp_ffn_inp", il);
+
+    cur = build_norm(ffn_inp, layer.ffn_norm, nullptr, LLM_NORM_RMS, il);
+    cb(cur, "mtp_ffn_norm", il);
+
+    GGML_ASSERT(layer.ffn_gate && layer.ffn_down && layer.ffn_up && "MIMO2 MTP requires dense FFN tensors");
+    cur = build_ffn(cur,
+            layer.ffn_up,   layer.ffn_up_b,   nullptr,
+            layer.ffn_gate, layer.ffn_gate_b, nullptr,
+            layer.ffn_down, layer.ffn_down_b, nullptr,
+            nullptr,
+            LLM_FFN_SILU, LLM_FFN_PAR, il);
+    cb(cur, "mtp_ffn_out", il);
+
+    cur = ggml_add(ctx0, cur, ffn_inp);
+    cb(cur, "mtp_post_ffn", il);
+
+    cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+
+    cb(cur, "h_nextn", -1);
+    res->t_h_nextn = cur;
+
+    ggml_tensor * head_norm_w = layer.nextn.shared_head_norm
+            ? layer.nextn.shared_head_norm
+            : (layer.layer_out_norm ? layer.layer_out_norm : model.output_norm);
+    GGML_ASSERT(head_norm_w && "MIMO2 MTP missing head norm fallback");
+    cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1);
+    cb(cur, "mtp_shared_head_norm", -1);
+
+    ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output;
+    ggml_tensor * head_s = layer.nextn.shared_head_head ? layer.nextn.shared_head_head_s : model.output_s;
+    GGML_ASSERT(head_w && "MIMO2 MTP missing LM head fallback");
+    cur = build_lora_mm(head_w, cur, head_s);
+    cb(cur, "result_output", -1);
+
+    res->t_logits = cur;
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/models.h b/src/models/models.h
index c73136f3bd..bb372ece81 100644
--- a/src/models/models.h
+++ b/src/models/models.h
@@ -2127,6 +2127,10 @@ struct llama_model_mimo2 : public llama_model_base {
         graph(const llama_model & model, const llm_graph_params & params);
     };
 
+    struct graph_mtp : public llm_graph_context {
+        graph_mtp(const llama_model & model, const llm_graph_params & params);
+    };
+
     std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };