llama : add support for Nemotron 3 Super (#20411)

* llama : add support for Nemotron 3 Super

This commit adds support for the Nemotron 3 Super model (120B.A12B)
enabling this model to be converted to GGUF format and run in llama.cpp.

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Matt Clayton <156335168+mattjcly@users.noreply.github.com>

convert_hf_to_gguf.py

@@ -9743,20 +9743,35 @@ class NemotronHModel(GraniteHybridModel):
         # M: Mamba2, *: Attention, -: MLP
         # MoE:
         # M: Mamba2, *: Attention, E: Expert
-        hybrid_override_pattern = self.hparams["hybrid_override_pattern"]
+        pattern = self.hparams.get("hybrid_override_pattern") or self.hparams.get("layers_block_type")
-        self._ssm_layers = [i for i, val in enumerate(hybrid_override_pattern) if val == "M"]
+        if pattern is None:
-        self._mlp_layers = [i for i, val in enumerate(hybrid_override_pattern) if val == ("E" if self.is_moe else "-")]
+            self._ssm_layers = []
+            self._mlp_layers = []
+        elif isinstance(pattern, str):
+            self._ssm_layers = [i for i, val in enumerate(pattern) if val == "M"]
+            self._mlp_layers = [i for i, val in enumerate(pattern) if val == ("E" if self.is_moe else "-")]
+        else:
+            self._ssm_layers = [i for i, val in enumerate(pattern) if val == "mamba"]
+            self._mlp_layers = [i for i, val in enumerate(pattern) if val == "moe"]
 
     def get_attn_layers(self):
-        hybrid_override_pattern = self.hparams["hybrid_override_pattern"]
+        pattern = self.hparams.get("hybrid_override_pattern") or self.hparams.get("layers_block_type")
-        assert len(hybrid_override_pattern) == self.block_count, "Mismatch between hybrid override and num_hidden_layers!"
+        if pattern is None:
-        return [i for i, val in enumerate(hybrid_override_pattern) if val == "*"]
+            return []
+        assert len(pattern) == self.block_count, f"Mismatch between pattern ({len(pattern)}) and block_count ({self.block_count})!"
+        if isinstance(pattern, str):
+            return [i for i, val in enumerate(pattern) if val == "*"]
+
+        return [i for i, val in enumerate(pattern) if val == "attention"]
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
 
-        self.gguf_writer.add_key_length(self.head_dim)
+        head_dim = self.head_dim
-        self.gguf_writer.add_value_length(self.head_dim)
+        if head_dim is None:
+            raise ValueError("Could not find the attention head dim in config")
+        self.gguf_writer.add_key_length(head_dim)
+        self.gguf_writer.add_value_length(head_dim)
 
         # Set feed_forward_length
         # NOTE: This will trigger an override warning. This is preferable to
@@ -9784,6 +9799,9 @@ class NemotronHModel(GraniteHybridModel):
             if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
                 self.gguf_writer.add_expert_used_count(n_experts_used)
 
+            if (latent_size := self.hparams.get("moe_latent_size")) is not None:
+                self.gguf_writer.add_moe_latent_size(latent_size)
+
     def set_vocab(self):
         super().set_vocab()
 
@@ -9803,6 +9821,13 @@ class NemotronHModel(GraniteHybridModel):
             name = name[len("language_model."):]
 
         if self.is_moe and bid is not None:
+            # Skip Multi-Token Prediction (MTP) tensors. These are used for
+            # for speculative decoding but we don't include them in this model
+            # conversion. See https://github.com/ggml-org/llama.cpp/pull/18886
+            if "mtp" in name:
+                logger.info(f"gguf: Skipping MTP (Speculative) layer: {name}")
+                return []
+
             if name.endswith("mixer.gate.e_score_correction_bias"):
                 new_name = name.replace("e_score_correction_bias", "e_score_correction.bias")
                 yield from ModelBase.modify_tensors(self, data_torch, new_name, bid)

ggml/src/ggml-metal/ggml-metal.metal

@@ -9081,6 +9081,7 @@ template [[host_name("kernel_mul_mm_id_map0_ne20_6" )]] kernel kernel_mul_mm_id_
 template [[host_name("kernel_mul_mm_id_map0_ne20_8" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<8>;
 template [[host_name("kernel_mul_mm_id_map0_ne20_10")]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<10>;
 template [[host_name("kernel_mul_mm_id_map0_ne20_16")]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<16>;
+template [[host_name("kernel_mul_mm_id_map0_ne20_22")]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<22>;
 
 template<typename S0, typename S0_4x4, typename S0_8x8, typename S1, typename S1_2x4, typename S1_8x8, typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread S0_4x4 &), typename T0, typename T0_4x4, typename T1, typename T1_2x4>
 kernel void kernel_mul_mm_id(

gguf-py/gguf/constants.py

@@ -125,6 +125,7 @@ class Keys:
         EXPERT_GROUP_SCALE                = "{arch}.expert_group_scale"
         EXPERTS_PER_GROUP                 = "{arch}.experts_per_group"
         MOE_EVERY_N_LAYERS                = "{arch}.moe_every_n_layers"
+        MOE_LATENT_SIZE                   = "{arch}.moe_latent_size"
         NEXTN_PREDICT_LAYERS              = "{arch}.nextn_predict_layers"
         NUM_DEEPSTACK_LAYERS              = "{arch}.n_deepstack_layers"
         POOLING_TYPE                      = "{arch}.pooling_type"
@@ -543,6 +544,8 @@ class MODEL_TENSOR(IntEnum):
     FFN_DOWN_CHEXP       = auto()
     FFN_UP_CHEXP         = auto()
     FFN_EXP_PROBS_B      = auto()
+    MOE_LATENT_DOWN      = auto() # nemotron 3 super
+    MOE_LATENT_UP        = auto() # nemotron 3 super
     ATTN_Q_NORM          = auto()
     ATTN_K_NORM          = auto()
     LAYER_OUT_NORM       = auto()
@@ -986,6 +989,8 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.FFN_UP_EXP:                "blk.{bid}.ffn_up_exps",
     MODEL_TENSOR.FFN_GATE_UP_EXP:           "blk.{bid}.ffn_gate_up_exps",
     MODEL_TENSOR.FFN_EXP_PROBS_B:           "blk.{bid}.exp_probs_b",
+    MODEL_TENSOR.MOE_LATENT_DOWN:           "blk.{bid}.ffn_latent_down",      # nemotron 3 super
+    MODEL_TENSOR.MOE_LATENT_UP:             "blk.{bid}.ffn_latent_up",        # nemotron 3 super
     MODEL_TENSOR.LAYER_OUT_NORM:            "blk.{bid}.layer_output_norm",
     MODEL_TENSOR.PER_LAYER_TOKEN_EMBD:      "per_layer_token_embd",           # gemma3n
     MODEL_TENSOR.PER_LAYER_MODEL_PROJ:      "per_layer_model_proj",           # gemma3n
@@ -2913,6 +2918,9 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_GATE_INP,
         MODEL_TENSOR.FFN_UP_EXP,
         MODEL_TENSOR.FFN_DOWN_EXP,
+        # expert latent
+        MODEL_TENSOR.MOE_LATENT_DOWN,
+        MODEL_TENSOR.MOE_LATENT_UP,
         # shared expert
         MODEL_TENSOR.FFN_DOWN_SHEXP,
         MODEL_TENSOR.FFN_UP_SHEXP,

gguf-py/gguf/gguf_writer.py

@@ -859,6 +859,9 @@ class GGUFWriter:
     def add_moe_every_n_layers(self, value: int) -> None:
         self.add_uint32(Keys.LLM.MOE_EVERY_N_LAYERS.format(arch=self.arch), value)
 
+    def add_moe_latent_size(self, value: int) -> None:
+        self.add_uint32(Keys.LLM.MOE_LATENT_SIZE.format(arch=self.arch), value)
+
     def add_nextn_predict_layers(self, count: int) -> None:
         self.add_uint32(Keys.LLM.NEXTN_PREDICT_LAYERS.format(arch=self.arch), count)
 

gguf-py/gguf/tensor_mapping.py

@@ -571,6 +571,14 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.experts.gate_up_proj",
         ),
 
+        MODEL_TENSOR.MOE_LATENT_DOWN: (
+            "backbone.layers.{bid}.mixer.fc1_latent_proj",                 # nemotron 3 super
+        ),
+
+        MODEL_TENSOR.MOE_LATENT_UP: (
+            "backbone.layers.{bid}.mixer.fc2_latent_proj",                 # nemotron 3 super
+        ),
+
         # Feed-forward down
         MODEL_TENSOR.FFN_DOWN: (
             "gpt_neox.layers.{bid}.mlp.dense_4h_to_h",                # gptneox

src/llama-arch.cpp

@@ -185,6 +185,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_EXPERT_GROUP_SCALE,                "%s.expert_group_scale"                },
     { LLM_KV_EXPERTS_PER_GROUP,                 "%s.experts_per_group"                 },
     { LLM_KV_MOE_EVERY_N_LAYERS,                "%s.moe_every_n_layers"                },
+    { LLM_KV_MOE_LATENT_SIZE,                   "%s.moe_latent_size"                   },
     { LLM_KV_NEXTN_PREDICT_LAYERS,              "%s.nextn_predict_layers"              },
     { LLM_KV_NUM_DEEPSTACK_LAYERS,              "%s.n_deepstack_layers"                },
     { LLM_KV_POOLING_TYPE,                      "%s.pooling_type"                      },
@@ -365,6 +366,8 @@ static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
     { LLM_TENSOR_FFN_UP_SHEXP,                           "blk.%d.ffn_up_shexp" },
     { LLM_TENSOR_FFN_DOWN_SHEXP,                         "blk.%d.ffn_down_shexp" },
     { LLM_TENSOR_FFN_EXP_PROBS_B,                        "blk.%d.exp_probs_b" },
+    { LLM_TENSOR_FFN_LATENT_DOWN,                        "blk.%d.ffn_latent_down" },
+    { LLM_TENSOR_FFN_LATENT_UP,                          "blk.%d.ffn_latent_up" },
     { LLM_TENSOR_ATTN_NORM_2,                            "blk.%d.attn_norm_2" },
     { LLM_TENSOR_ATTN_QKV,                               "blk.%d.attn_qkv" },
     { LLM_TENSOR_LAYER_OUT_NORM,                         "blk.%d.layer_output_norm" },
@@ -1879,6 +1882,8 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                 LLM_TENSOR_FFN_UP_EXPS,
                 LLM_TENSOR_FFN_DOWN_EXPS,
                 LLM_TENSOR_FFN_EXP_PROBS_B,
+                LLM_TENSOR_FFN_LATENT_DOWN,
+                LLM_TENSOR_FFN_LATENT_UP,
                 // MoE shared expert layer
                 LLM_TENSOR_FFN_DOWN_SHEXP,
                 LLM_TENSOR_FFN_UP_SHEXP,
@@ -2754,6 +2759,9 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     {LLM_TENSOR_NEXTN_HNORM,                {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
     {LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD,     {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_NEXTN_SHARED_HEAD_NORM,     {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
+    // Nemotron 3 Super
+    {LLM_TENSOR_FFN_LATENT_DOWN,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_FFN_LATENT_UP,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
 };
 
 LLM_KV::LLM_KV(llm_arch arch, const char * suffix) : arch(arch), suffix(suffix) {}

src/llama-arch.h

@@ -189,6 +189,7 @@ enum llm_kv {
     LLM_KV_EXPERT_GROUP_SCALE,
     LLM_KV_EXPERTS_PER_GROUP,
     LLM_KV_MOE_EVERY_N_LAYERS,
+    LLM_KV_MOE_LATENT_SIZE,
     LLM_KV_NEXTN_PREDICT_LAYERS,
     LLM_KV_NUM_DEEPSTACK_LAYERS,
     LLM_KV_POOLING_TYPE,
@@ -385,6 +386,8 @@ enum llm_tensor {
     LLM_TENSOR_FFN_GATE_CHEXPS,
     LLM_TENSOR_FFN_UP_CHEXPS,
     LLM_TENSOR_FFN_EXP_PROBS_B,
+    LLM_TENSOR_FFN_LATENT_DOWN,
+    LLM_TENSOR_FFN_LATENT_UP,
     LLM_TENSOR_ATTN_Q_NORM,
     LLM_TENSOR_ATTN_K_NORM,
     LLM_TENSOR_LAYER_OUT_NORM,

src/llama-hparams.h

@@ -89,6 +89,7 @@ struct llama_hparams {
     bool     expert_weights_norm  = false;
     uint32_t expert_gating_func   = LLAMA_EXPERT_GATING_FUNC_TYPE_NONE;
     uint32_t moe_every_n_layers   = 0;
+    uint32_t moe_latent_size      = 0;
     uint32_t nextn_predict_layers = 0;
 
     float f_norm_eps;

src/llama-model.cpp

@@ -135,6 +135,7 @@ const char * llm_type_name(llm_type type) {
         case LLM_TYPE_100B_A6B:      return "100B.A6B";
         case LLM_TYPE_102B_A12B:     return "102B.A12B";
         case LLM_TYPE_106B_A12B:     return "106B.A12B";
+        case LLM_TYPE_120B_A12B:     return "120B.A12B";
         case LLM_TYPE_122B_A10B:     return "122B.A10B";
         case LLM_TYPE_196B_A11B:     return "196B.A11B";
         case LLM_TYPE_230B_A10B:     return "230B.A10B";
@@ -1861,10 +1862,12 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,               hparams.n_expert_shared, false);
                 ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,               hparams.expert_weights_norm, false);
                 ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,              hparams.expert_weights_scale, false);
+                ml.get_key(LLM_KV_MOE_LATENT_SIZE,                   hparams.moe_latent_size, false);
 
                 switch (hparams.n_layer) {
                     case 52: type = LLM_TYPE_31B_A3_5B; break; // Nemotron-H_MOE 31B
                     case 56: type = LLM_TYPE_9B; break;
+                    case 88: type = LLM_TYPE_120B_A12B; break;
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
@@ -5544,6 +5547,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                     const int64_t n_ssm_head = hparams.ssm_dt_rank;
                     const int64_t n_group    = hparams.ssm_n_group;
                     const int64_t d_in_proj  = 2*d_inner + 2*n_group*d_state + n_ssm_head;
+                    const int64_t moe_n_embd = hparams.moe_latent_size > 0 ? hparams.moe_latent_size : n_embd;
 
                     // embeddings
                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -5603,8 +5607,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                                 layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert         }, 0);
 
                                 // MoE branch
-                                layer.ffn_down_exps   = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
+                                layer.ffn_latent_down = create_tensor(tn(LLM_TENSOR_FFN_LATENT_DOWN, "weight", i), {n_embd, moe_n_embd}, TENSOR_NOT_REQUIRED);
-                                layer.ffn_up_exps     = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+                                layer.ffn_latent_up   = create_tensor(tn(LLM_TENSOR_FFN_LATENT_UP,   "weight", i), {moe_n_embd, n_embd}, TENSOR_NOT_REQUIRED);
+
+                                layer.ffn_down_exps   = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   moe_n_embd, n_expert}, 0);
+                                layer.ffn_up_exps     = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {moe_n_embd, n_ff_exp, n_expert}, 0);
 
                                 // Shared expert branch
                                 layer.ffn_down_shexp  = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0);

src/llama-model.h

@@ -126,6 +126,7 @@ enum llm_type {
     LLM_TYPE_100B_A6B,
     LLM_TYPE_102B_A12B, // Solar-Open
     LLM_TYPE_106B_A12B, // GLM-4.5-Air
+    LLM_TYPE_120B_A12B, // Nemotron 3 Super
     LLM_TYPE_122B_A10B, // Qwen3.5
     LLM_TYPE_196B_A11B, // Step3.5-Flash
     LLM_TYPE_230B_A10B, // Minimax M2
@@ -294,6 +295,10 @@ struct llama_layer {
     struct ggml_tensor * ffn_up_exps_b     = nullptr;
     struct ggml_tensor * ffn_gate_up_exps_b = nullptr;
 
+    // ff MoE latent proj
+    struct ggml_tensor * ffn_latent_down = nullptr;
+    struct ggml_tensor * ffn_latent_up   = nullptr;
+
     // ff shared expert (shexp)
     struct ggml_tensor * ffn_gate_inp_shexp = nullptr;
     struct ggml_tensor * ffn_gate_shexp     = nullptr;

src/models/nemotron-h.cpp

@@ -114,9 +114,18 @@ ggml_tensor * llm_build_nemotron_h::build_ffn_layer(ggml_tensor * cur, const lla
                 LLM_FFN_RELU_SQR, LLM_FFN_PAR, il);
         cb(cur, "ffn_out", il);
     } else {
-        ggml_tensor * ffn_inp = cur;
+        ggml_tensor * inp_emb    = cur;
+        ggml_tensor * inp_latent = cur;
+
+        if (model.layers[il].ffn_latent_down) {
+            inp_latent = ggml_mul_mat(ctx0, model.layers[il].ffn_latent_down, cur);
+        }
+
+        ggml_tensor * router_logits = build_lora_mm(model.layers[il].ffn_gate_inp, cur);
+        cb(router_logits, "ffn_moe_logits", il);
+
         ggml_tensor * moe_out =
-            build_moe_ffn(ffn_inp,
+            build_moe_ffn(inp_latent,
                     model.layers[il].ffn_gate_inp,
                     model.layers[il].ffn_up_exps,
                     nullptr, // no gate
@@ -126,10 +135,15 @@ ggml_tensor * llm_build_nemotron_h::build_ffn_layer(ggml_tensor * cur, const lla
                     LLM_FFN_RELU_SQR, hparams.expert_weights_norm,
                     hparams.expert_weights_scale,
                     LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID,
-                    il);
+                    il,
+                    router_logits);
         cb(moe_out, "ffn_moe_out", il);
 
-        ggml_tensor * ffn_shexp = build_ffn(ffn_inp,
+        if (model.layers[il].ffn_latent_up) {
+            moe_out = ggml_mul_mat(ctx0, model.layers[il].ffn_latent_up, moe_out);
+        }
+
+        ggml_tensor * ffn_shexp = build_ffn(inp_emb,
                     model.layers[il].ffn_up_shexp,  NULL, NULL,
                     NULL /* no gate */           ,  NULL, NULL,
                     model.layers[il].ffn_down_shexp, NULL, NULL,