diff --git a/ggml/src/iqk/iqk_mul_mat.cpp b/ggml/src/iqk/iqk_mul_mat.cpp
index 4fac11e3..de302a1f 100644
--- a/ggml/src/iqk/iqk_mul_mat.cpp
+++ b/ggml/src/iqk/iqk_mul_mat.cpp
@@ -1510,8 +1510,12 @@ void iqk_fused_delta_net_impl(int n_heads, int n_tokens, int n_seqs,
 
     const float scale = 1.0f / sqrtf((float) head_dim);
 
+#ifdef __AVX512F__
+    __m512 v_prime[head_dim/16], out_val[head_dim/16];
+#else
     float v_new_buf[head_dim];
     float v_prime[head_dim], out_val[head_dim];
+#endif
 
     for (int h_idx = h_start; h_idx < h_end; ++h_idx) {
         const int batch_idx = h_idx / n_heads;
@@ -1539,7 +1543,15 @@ void iqk_fused_delta_net_impl(int n_heads, int n_tokens, int n_seqs,
             const float beta_raw = beta_data[g_head_offset + t];
 
             float kq_sum    = 0.0f;
-#ifdef __AVX2__
+#if defined __AVX512F__
+            auto vqksum = _mm512_setzero_ps();
+            for (int i = 0; i < head_dim; i += 16) {
+                auto vq = _mm512_loadu_ps(q_t + i);
+                auto vk = _mm512_loadu_ps(k_t + i);
+                vqksum = _mm512_fmadd_ps(vk, vq, vqksum);
+            }
+            kq_sum = _mm512_reduce_add_ps(vqksum);
+#elif defined __AVX2__
             auto vqksum = _mm256_setzero_ps();
             for (int i = 0; i < head_dim; i += 8) {
                 auto vq = _mm256_loadu_ps(q_t + i);
@@ -1560,6 +1572,42 @@ void iqk_fused_delta_net_impl(int n_heads, int n_tokens, int n_seqs,
 
             float * out_t = out_data + out_head_offset + t * out_token_stride;
 
+#ifdef __AVX512F__
+            for (int j = 0; j < head_dim/16; ++j) {
+                v_prime[j] = out_val[j] = _mm512_setzero_ps();
+            }
+            for (int col = 0; col < head_dim; ++col) {
+                auto k_col = _mm512_set1_ps(k_t[col]);
+                auto q_col = _mm512_set1_ps(q_t[col]);
+                for (int j = 0; j < head_dim/16; ++j) {
+                    auto s = _mm512_loadu_ps(state + col * head_dim + 16*j);
+                    v_prime[j] = _mm512_fmadd_ps(s, k_col, v_prime[j]);
+                    out_val[j] = _mm512_fmadd_ps(s, q_col, out_val[j]);
+                }
+            }
+            auto c1 = _mm512_set1_ps(beta_val);
+            auto c2 = _mm512_set1_ps(beta_val*decay);
+            auto c3 = _mm512_set1_ps(decay*scale);
+            auto c4 = _mm512_set1_ps(attn_score);
+            for (int j = 0; j < head_dim/16; ++j) {
+                auto v = _mm512_loadu_ps(v_t + 16*j);
+                v_prime[j] = _mm512_sub_ps(_mm512_mul_ps(v, c1), _mm512_mul_ps(v_prime[j], c2));
+                auto oval = _mm512_fmadd_ps(v_prime[j], c4, _mm512_mul_ps(out_val[j], c3));
+                _mm512_storeu_ps(out_t + 16*j, oval);
+            }
+            auto vmin = _mm512_set1_ps(-1e6f);
+            auto vmax = _mm512_set1_ps( 1e6f);
+            auto vd   = _mm512_set1_ps(decay);
+            for (int col = 0; col < head_dim; ++col) {
+                auto vk = _mm512_set1_ps(k_t[col]);
+                for (int j = 0; j < head_dim/16; ++j) {
+                    auto vs = _mm512_loadu_ps(state + col * head_dim + 16*j);
+                    vs = _mm512_fmadd_ps(v_prime[j], vk, _mm512_mul_ps(vs, vd));
+                    vs = _mm512_max_ps(vmin, _mm512_min_ps(vmax, vs));
+                    _mm512_storeu_ps(state + col * head_dim + 16*j, vs);
+                }
+            }
+#else
             std::memset(v_prime, 0, head_dim*sizeof(float));
             std::memset(out_val, 0, head_dim*sizeof(float));
             for (int col = 0; col < head_dim; ++col) {
@@ -1603,6 +1651,7 @@ void iqk_fused_delta_net_impl(int n_heads, int n_tokens, int n_seqs,
                     state[row + col * head_dim] = fminf(fmaxf(s, -1e6f), 1e6f);
                 }
             }
+#endif
 #endif
         }
     }
diff --git a/src/llama-delta-net.cpp b/src/llama-delta-net.cpp
index bed2780a..092a2908 100644
--- a/src/llama-delta-net.cpp
+++ b/src/llama-delta-net.cpp
@@ -134,12 +134,12 @@ std::pair<ggml_tensor *, ggml_tensor *> delta_net::build_fused_delta_net(ggml_co
     const int64_t output_size = S_v * H_v * n_tokens * n_seqs;
     const int64_t state_size  = S_v * S_v * H_v * n_seqs;
 
-    ggml_tensor * output_tokens = ggml_view_4d(ctx0, fused_result,
+    auto output_tokens = ggml_view_4d(ctx0, fused_result,
             S_v, H_v, n_tokens, n_seqs,
             ggml_row_size(fused_result->type, S_v),
             ggml_row_size(fused_result->type, S_v * H_v),
             ggml_row_size(fused_result->type, S_v * H_v * n_tokens), 0);
-    output_tokens = ggml_cont_4d(ctx0, output_tokens, S_v, H_v, n_tokens, n_seqs);
+    //output_tokens = ggml_cont_4d(ctx0, output_tokens, S_v, H_v, n_tokens, n_seqs);
 
     ggml_tensor * new_state_flat = ggml_view_1d(ctx0, fused_result, state_size,
             output_size * ggml_element_size(fused_result));