diff --git a/src/models/nemotron-h-moe.cpp b/src/models/nemotron-h-moe.cpp
index 4d03f49e0f..b4fb254300 100644
--- a/src/models/nemotron-h-moe.cpp
+++ b/src/models/nemotron-h-moe.cpp
@@ -100,6 +100,18 @@ llama_model_nemotron_h_moe::graph_mtp::graph_mtp(const llama_model & model, cons
         ggml_tensor * router_logits = build_lora_mm(layer.ffn_gate_inp, cur);
         cb(router_logits, "mtp_ffn_moe_logits", il);
 
+        ggml_tensor * ffn_shexp = build_ffn(cur,
+                layer.ffn_up_shexp,   NULL, layer.ffn_up_shexp_s,
+                NULL,                 NULL, NULL,
+                layer.ffn_down_shexp, NULL, layer.ffn_down_shexp_s,
+                NULL,
+                LLM_FFN_RELU_SQR, LLM_FFN_PAR, il);
+        cb(ffn_shexp, "mtp_ffn_shexp", il);
+
+        if (layer.ffn_latent_down) {
+            cur = ggml_mul_mat(ctx0, layer.ffn_latent_down, cur);
+        }
+
         ggml_tensor * moe_out =
             build_moe_ffn(cur,
                 layer.ffn_gate_inp,
@@ -118,13 +130,9 @@ llama_model_nemotron_h_moe::graph_mtp::graph_mtp(const llama_model & model, cons
                 layer.ffn_down_exps_s);
         cb(moe_out, "mtp_ffn_moe_out", il);
 
-        ggml_tensor * ffn_shexp = build_ffn(cur,
-                layer.ffn_up_shexp,   NULL, layer.ffn_up_shexp_s,
-                NULL,                 NULL, NULL,
-                layer.ffn_down_shexp, NULL, layer.ffn_down_shexp_s,
-                NULL,
-                LLM_FFN_RELU_SQR, LLM_FFN_PAR, il);
-        cb(ffn_shexp, "mtp_ffn_shexp", il);
+        if (layer.ffn_latent_up) {
+            moe_out = ggml_mul_mat(ctx0, layer.ffn_latent_up, moe_out);
+        }
 
         cur = ggml_add(ctx0, moe_out, ffn_shexp);
         cb(cur, "mtp_ffn_out", il);
diff --git a/src/models/nemotron-h.cpp b/src/models/nemotron-h.cpp
index 24ed9a673e..be27650b06 100644
--- a/src/models/nemotron-h.cpp
+++ b/src/models/nemotron-h.cpp
@@ -172,6 +172,8 @@ void llama_model_nemotron_h::load_arch_tensors(llama_model_loader & ml) {
         layer.attn_post_norm  = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM,  "weight", i), {n_embd}, mtp_flags);
         layer.ffn_gate_inp    = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,    "weight", i), {n_embd, n_expert}, mtp_flags);
         layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias",   i), {n_expert}, mtp_flags);
+        layer.ffn_latent_down = create_tensor(tn(LLM_TENSOR_FFN_LATENT_DOWN, "weight", i), {n_embd, moe_n_embd}, mtp_flags | TENSOR_NOT_REQUIRED);
+        layer.ffn_latent_up   = create_tensor(tn(LLM_TENSOR_FFN_LATENT_UP,   "weight", i), {moe_n_embd, n_embd}, mtp_flags | 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}, mtp_flags);
         layer.ffn_up_exps     = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,     "weight", i), {moe_n_embd, n_ff_exp,   n_expert}, mtp_flags);
         layer.ffn_down_shexp  = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP,  "weight", i), {n_ff_shexp, n_embd}, mtp_flags);