diff --git a/common/common.cpp b/common/common.cpp
index 942379d6..f61cb93b 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1533,7 +1533,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
     }
     if (arg == "-fdn" || arg == "--fused-delta-net") {
         CHECK_ARG
-        params.fused_delta_net = std::stoi(argv[i]);
+        fprintf(stderr, "=================== %s has been deprecated\n", arg.c_str());
         return true;
     }
     if (arg == "-smf16" || arg == "--split-mode-f16") {
@@ -2276,7 +2276,6 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
     options.push_back({ "*",         "-grt, --graph-reduce-type",       "Type for data exchange between GPUs (default: %s)", "f32"});
     options.push_back({ "*",         "-smgs, --split-mode-graph-scheduling,", "Force Split Mode Graph Scheduling (default: %d)", params.split_mode_graph_scheduling});
     options.push_back({ "*",         "-sas,  --scheduler_async,",       "Async evaluation of compute graphs: %d)", params.scheduler_async});
-    options.push_back({ "*",         "-fdn,  --fused-delta-net N",      "Use fused delta-net when batch size is <= N with recurrent models: %d)", params.fused_delta_net});
     options.push_back({ "*",         "-vq, --validate-quants",          "validate quantized data while loading the model (default: %d)", params.validate_quants});
     options.push_back({ "*",           "-p,    --prompt PROMPT",        "prompt to start generation with\n"
                                                                         "in conversation mode, this will be used as system prompt\n"
@@ -3355,7 +3354,6 @@ struct llama_context_params common_context_params_to_llama(const gpt_params & pa
     cparams.split_mode_graph_scheduling = params.split_mode_graph_scheduling;
     //cparams.split_mode_f16    = params.split_mode_f16;
     cparams.scheduler_async   = params.scheduler_async;
-    cparams.fused_delta_net   = params.fused_delta_net;
     cparams.min_experts       = params.min_experts;
     cparams.thresh_experts    = params.thresh_experts;
     cparams.only_active_experts = params.only_active_exps;
@@ -4366,7 +4364,6 @@ void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const l
     //fprintf(stream, "split_mode_f16: %s # default: true\n", params.split_mode_f16 ? "true" : "false");
     fprintf(stream, "reduce_type: %s # default f16\n", params.reduce_type.c_str());
     fprintf(stream, "scheduler_async: %s # default: false\n", params.scheduler_async ? "true" : "false");
-    fprintf(stream, "fused_delta_net: %d # default: 0\n", params.fused_delta_net );
     fprintf(stream, "ser: %d,%g # defaulr: -1,0\n", params.min_experts, params.thresh_experts);
     fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp);
 
diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp
index aeb78f89..c93fd336 100644
--- a/examples/llama-bench/llama-bench.cpp
+++ b/examples/llama-bench/llama-bench.cpp
@@ -371,7 +371,6 @@ static void print_usage(int /* argc */, char ** argv) {
     printf("  -no-fug, --no-fused-up-gate <0|1>   (default: %s)\n", cmd_params_defaults.no_fug? "1" : "0");
     printf("  -no-ooae, --no-offload-only-active-experts <0|1>   (default: %s)\n", cmd_params_defaults.no_ooae? "1" : "0");
     printf("  -sas, --scheduler-async <0|1>       (default: %s)\n", cmd_params_defaults.sas ? "1" : "0");
-    printf("  -fdn, --fused-delta-net <n>         (default: %d)\n", cmd_params_defaults.fdn);
     printf("        --print-overrides <0|1>       (default: %s)\n", cmd_params_defaults.print_overrides ? "1" : "0");
     printf("\n");
     printf("Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n");
@@ -813,12 +812,6 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
                 break;
             }
             params.sas = std::stoi(argv[i]);
-        } else if (arg == "-fdn" || arg == "--fused-delta-net") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.fdn = std::stoi(argv[i]);
         } else if (arg == "-rcache" || arg == "--rope-cache") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -965,7 +958,6 @@ struct cmd_params_instance {
     bool muge = false;
     bool rcache = false;
     bool sas = false;
-    int fdn = 0;
     const llama_model_tensor_buft_override* buft_overrides;
 
     llama_model_params to_llama_mparams() const {
@@ -1001,7 +993,6 @@ struct cmd_params_instance {
                muge == other.muge &&
                use_thp == other.use_thp &&
                sas == other.sas &&
-               fdn == other.fdn &&
                tensor_split == other.tensor_split;
     }
 
@@ -1028,7 +1019,6 @@ struct cmd_params_instance {
         cparams.embeddings = embeddings;
         cparams.cuda_params = (void *)cuda_params.data();
         cparams.scheduler_async = sas;
-        cparams.fused_delta_net = fdn;
 
         return cparams;
     }
@@ -1095,7 +1085,6 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .muge         = */ params.muge,
                 /* .rcache       = */ params.rcache,
                 /* .sas          = */ params.sas,
-                /* .fdn          = */ params.fdn,
                 /* .buft_overrides=*/ params.buft_overrides.data(),
             };
             instances.push_back(instance);
@@ -1139,7 +1128,6 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .muge         = */ params.muge,
                 /* .rcache       = */ params.rcache,
                 /* .sas          = */ params.sas,
-                /* .fdn          = */ params.fdn,
                 /* .buft_overrides=*/ params.buft_overrides.data(),
             };
             instances.push_back(instance);
@@ -1183,7 +1171,6 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .muge         = */ params.muge,
                 /* .rcache       = */ params.rcache,
                 /* .sas          = */ params.sas,
-                /* .fdn          = */ params.fdn,
                 /* .buft_overrides=*/ params.buft_overrides.data(),
             };
             instances.push_back(instance);
@@ -1227,7 +1214,6 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .muge         = */ params.muge,
                 /* .rcache       = */ params.rcache,
                 /* .sas          = */ params.sas,
-                /* .fdn          = */ params.fdn,
                 /* .buft_overrides=*/ params.buft_overrides.data(),
             };
             instances.push_back(instance);
@@ -1282,7 +1268,6 @@ struct test {
     bool muge = false;
     bool rcache = false;
     bool sas = false;
-    int fdn = 0;
     std::string override_tensor;
     int n_prompt;
     int n_gen;
@@ -1324,7 +1309,6 @@ struct test {
         ger = inst.ger;
         rcache = inst.rcache;
         sas = inst.sas;
-        fdn = inst.fdn;
         no_fug = inst.no_fug;
         use_thp = inst.use_thp;
         no_ooae = inst.no_ooae;
@@ -1429,7 +1413,7 @@ struct test {
             field == "model_size" || field == "model_n_params" ||
             field == "n_gpu_layers" || field == "main_gpu" ||
             field == "n_prompt" || field == "n_gen" || field == "mla_attn" || field == "attn_max_batch" ||
-            field == "avg_ns" || field == "stddev_ns" || field == "fdn") {
+            field == "avg_ns" || field == "stddev_ns") {
             return INT;
         }
         if (field == "cuda" || field == "vulkan" || field == "kompute" || field == "metal" ||
@@ -1480,7 +1464,7 @@ struct test {
             std::to_string(mla_attn), std::to_string(attn_max_batch), ser_to_string(ser), std::to_string(reuse),
             tensor_split_str, std::to_string(use_mmap), std::to_string(embeddings),
             std::to_string(repack), std::to_string(mqkv), std::to_string(muge), std::to_string(fmoe), std::to_string(ger),
-            std::to_string(no_fug), std::to_string(use_thp), std::to_string(no_ooae), std::to_string(rcache), std::to_string(sas), std::to_string(fdn),
+            std::to_string(no_fug), std::to_string(use_thp), std::to_string(no_ooae), std::to_string(rcache), std::to_string(sas),
             cuda_params, override_tensor,
             std::to_string(n_prompt), std::to_string(n_gen), test_time,
             std::to_string(avg_ns()), std::to_string(stdev_ns()),
@@ -1501,7 +1485,7 @@ struct test {
             "n_gpu_layers", "split_mode",
             "main_gpu", "no_kv_offload", "flash_attn", "mla_attn", "attn_max_batch", "ser", "reuse",
             "tensor_split", "use_mmap", "embeddings", "repack", "mqkv", "muge", "fused_moe", "grouped_er",
-            "no_fused_up_gate", "use_thp", "no_ooae", "rcache", "sas", "fdn", "cuda_params", "override_tensor",
+            "no_fused_up_gate", "use_thp", "no_ooae", "rcache", "sas", "cuda_params", "override_tensor",
             "n_prompt", "n_gen", "test_time",
             "avg_ns", "stddev_ns",
             "avg_ts", "stddev_ts", "test",
@@ -1691,9 +1675,6 @@ struct markdown_printer : public printer {
         if (field == "sas") {
             return 3;
         }
-        if (field == "fdn") {
-            return 4;
-        }
         if (field == "use_thp") {
             return 3;
         }
@@ -1767,9 +1748,6 @@ struct markdown_printer : public printer {
         if (field == "sas") {
             return "sas";
         }
-        if (field == "fdn") {
-            return "fdn";
-        }
         if (field == "use_thp") {
             return "thp";
         }
@@ -1880,9 +1858,6 @@ struct markdown_printer : public printer {
         if (params.sas != cmd_params_defaults.sas) {
             fields.emplace_back("sas");
         }
-        if (params.fdn != cmd_params_defaults.fdn) {
-            fields.emplace_back("fdn");
-        }
         if (params.muge != cmd_params_defaults.muge) {
             fields.emplace_back("muge");
         }
diff --git a/include/llama.h b/include/llama.h
index 338381b4..cd8da575 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -456,7 +456,6 @@ extern "C" {
         bool split_mode_graph_scheduling; // if true, force split mode graph scheduling
         //bool split_mode_f16;    // if true, cast intermediate results to f16 before copying to other GPUs
         bool scheduler_async;   // if true, with split mode "graph" graph evaluation will be done using multiple threads
-        int  fused_delta_net;
         bool mtp;   // Activate MTP if supported
         enum llama_mtp_op_type mtp_op_type;
 
diff --git a/src/llama-cparams.h b/src/llama-cparams.h
index 6ac0a3a3..b178059f 100644
--- a/src/llama-cparams.h
+++ b/src/llama-cparams.h
@@ -43,7 +43,6 @@ struct llama_cparams {
     bool split_mode_graph_scheduling;
     //bool split_mode_f16;
     bool scheduler_async;
-    int  fused_delta_net;
     int  min_experts;
     float thresh_experts;
     bool mtp;
diff --git a/src/llama-delta-net.cpp b/src/llama-delta-net.cpp
index 5aa6d1c7..26edb62e 100644
--- a/src/llama-delta-net.cpp
+++ b/src/llama-delta-net.cpp
@@ -74,304 +74,6 @@ delta_net::delta_net(llama_context & _lctx, const llama_batch & _batch) : lctx(_
 
 delta_net::~delta_net() = default;
 
-std::pair<ggml_tensor *, ggml_tensor *> delta_net::build_delta_net_chunking(ggml_context * ctx0,
-                      ggml_tensor * q, ggml_tensor * k, ggml_tensor * v,
-                      ggml_tensor * g, ggml_tensor * beta, ggml_tensor * state,
-                      ggml_tensor * causal_mask, ggml_tensor * identity,
-                      ggml_tensor * diag_mask, int il, const llm_build_cb & cb) {
-
-    const int64_t S_k      = q->ne[0];
-    const int64_t H_k      = q->ne[1];
-    const int64_t n_tokens = q->ne[2];
-    const int64_t n_seqs   = q->ne[3];
-
-    const int64_t S_v = v->ne[0];
-    const int64_t H_v = v->ne[1];
-
-    GGML_ASSERT(n_seqs == 1);
-    GGML_ASSERT(v->ne[2] == n_tokens);
-    GGML_ASSERT(k->ne[2] == n_tokens);
-    GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
-    if (beta->ne[0] != H_v || beta->ne[2] != n_tokens || beta->ne[3] != n_seqs) {
-        printf("beta: %ld x %ld x %ld, expected %ld x %ld x %ld\n", beta->ne[0], beta->ne[2], beta->ne[3], H_v, n_tokens, n_seqs);
-    }
-    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
-    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v && state->ne[2] == H_v && state->ne[3] == n_seqs);
-    GGML_ASSERT(H_k == H_v);
-
-    const float scale = 1.0f / sqrtf(S_v);
-    q = ggml_scale(ctx0, q, scale);
-
-    beta = ggml_sigmoid(ctx0, beta);
-
-    cb(q,    "q_in", il);
-    cb(k,    "k_in", il);
-    cb(v,    "v_in", il);
-    cb(beta, "beta_in", il);
-    cb(g,    "g_in", il);
-    cb(state,"state_in", il);
-
-    const int64_t chunk_size = QWEN3NEXT_CHUNK_SIZE;
-    const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
-    const int64_t n_chunks = (n_tokens + pad) / chunk_size;
-
-    q    = ggml_permute(ctx0, q, 0, 2, 1, 3);
-    k    = ggml_permute(ctx0, k, 0, 2, 1, 3);
-    v    = ggml_permute(ctx0, v, 0, 2, 1, 3);
-    g    = ggml_permute(ctx0, g, 2, 0, 3, 1);
-    beta = ggml_permute(ctx0, beta, 2, 0, 1, 3);
-
-    q    = ggml_pad(ctx0, q, 0, pad, 0, 0);
-    k    = ggml_pad(ctx0, k, 0, pad, 0, 0);
-    v    = ggml_pad(ctx0, v, 0, pad, 0, 0);
-    beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
-    g    = ggml_pad(ctx0, g, pad, 0, 0, 0);
-
-    cb(q,    "q_pad", il);
-    cb(k,    "k_pad", il);
-    cb(v,    "v_pad", il);
-    cb(beta, "beta_pad", il);
-    cb(g,    "g_pad", il);
-
-    ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
-    ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
-
-    cb(v_beta, "v_beta", il);
-    cb(k_beta, "k_beta", il);
-
-    q      = ggml_reshape_4d(ctx0, q,      S_k, chunk_size, n_chunks, H_k * n_seqs);
-    k      = ggml_reshape_4d(ctx0, k,      S_k, chunk_size, n_chunks, H_k * n_seqs);
-    k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_v * n_seqs);
-    v      = ggml_reshape_4d(ctx0, v,      S_v, chunk_size, n_chunks, H_v * n_seqs);
-    v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
-
-    g    = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_v * n_seqs);
-    beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_v * n_seqs);
-
-    ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
-    cb(g_cumsum, "g_cumsum", il);
-
-    ggml_tensor * gcs_i =
-        ggml_repeat_4d(ctx0, g_cumsum, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
-    ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
-
-    ggml_tensor * gcs_j_broadcast =
-        ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
-    ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
-    cb(decay_mask, "decay_mask", il);
-
-    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
-    cb(decay_mask, "decay_mask_1", il);
-    decay_mask = ggml_exp(ctx0, decay_mask);
-    cb(decay_mask, "decay_mask_exp", il);
-    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
-    cb(decay_mask, "decay_mask_2", il);
-
-    ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
-    cb(kmulkbeta, "kk_beta", il);
-
-    ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
-    cb(k_decay, "k_decay_1", il);
-    k_decay = ggml_mul(ctx0, k_decay, causal_mask);
-    cb(k_decay, "k_decay_2", il);
-    ggml_tensor * attn    = ggml_neg(ctx0, k_decay);
-    cb(attn, "attn_pre_solve", il);
-
-    ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
-    cb(attn_lower, "attn_lower", il);
-    ggml_tensor * identity_repeat =
-        ggml_repeat_4d(ctx0, identity, attn_lower->ne[0], attn_lower->ne[1], attn_lower->ne[2], attn_lower->ne[3]);
-    ggml_tensor * lhs        = ggml_neg(ctx0, ggml_sub(ctx0, attn_lower, identity_repeat));
-
-    ggml_tensor * lin_solve  = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
-    attn                     = ggml_mul(ctx0, lin_solve, causal_mask);
-    cb(attn, "attn_mul", il);
-    attn                     = ggml_add(ctx0, attn, identity);
-    cb(attn, "attn_solved", il);
-
-    auto v_beta_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_beta));
-    cb(v_beta_t, "v_beta_t", il);
-    v = ggml_mul_mat(ctx0, v_beta_t, attn);
-    cb(v, "v_beta", il);
-
-    ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum));
-    cb(g_cumsum_t, "g_cumsum_t", il);
-    ggml_tensor * gexp       = ggml_exp(ctx0, g_cumsum_t);
-    cb(gexp, "gexp", il);
-
-    ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp);
-    cb(kbeta_gexp, "kbeta_gexp", il);
-
-    auto kbeta_gexp_t = ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp));
-    cb(kbeta_gexp_t, "kbeta_gexp_t", il);
-    auto attn_kbeta = ggml_mul_mat(ctx0, attn, kbeta_gexp_t);
-    cb(attn_kbeta, "attn_kbeta", il);
-    ggml_tensor * k_cumdecay = ggml_cont(ctx0, ggml_transpose(ctx0, attn_kbeta));
-    cb(k_cumdecay, "k_cumdecay", il);
-
-    ggml_tensor * attn_kq = ggml_mul_mat(ctx0, k, q);
-    cb(attn_kq, "attn_kq_pre", il);
-    attn_kq = ggml_mul(ctx0, decay_mask, attn_kq);
-    cb(attn_kq, "attn_kq_0", il);
-    attn_kq = ggml_mul(ctx0, attn_kq,    diag_mask);
-    cb(attn_kq, "attn_kq", il);
-
-    ggml_tensor * g_last = ggml_view_4d(ctx0, g_cumsum, 1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
-            g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
-            (g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
-    g_last = ggml_cont(ctx0, g_last);
-    cb(g_last, "g_last", il);
-
-    ggml_tensor * g_last_exp = ggml_exp(ctx0, g_last);
-    cb(g_last_exp, "g_last_exp", il);
-
-    ggml_tensor * g_last_repeat =
-        ggml_repeat_4d(ctx0, g_last, chunk_size, 1, n_chunks, H_v * n_seqs);
-    ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last_repeat));
-    cb(g_diff, "g_diff", il);
-
-    ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
-    cb(g_diff_exp, "g_diff_exp", il);
-    ggml_tensor * g_diff_exp_t = ggml_reshape_4d(ctx0, g_diff_exp, 1, chunk_size, n_chunks, g_diff_exp->ne[3]);
-
-    ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp_t);
-    cb(key_gdiff, "key_gdiff", il);
-
-    ggml_tensor * key_gdiff_t = ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff));
-    cb(key_gdiff_t, "key_gdiff_t", il);
-
-    cb(state, "new_state", il);
-
-    auto get_slice_2d = [ctx0](ggml_tensor * t, int64_t c) -> ggml_tensor * {
-        return ggml_view_4d(ctx0, t, t->ne[0], t->ne[1], 1, t->ne[3],
-                t->nb[1], t->nb[2], t->nb[3], t->nb[2] * c);
-    };
-
-    ggml_tensor * core_attn_out = nullptr;
-
-    for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
-        ggml_tensor * q_chunk          = get_slice_2d(q, chunk);
-        ggml_tensor * v_chunk          = get_slice_2d(v, chunk);
-        ggml_tensor * gexp_chunk       = get_slice_2d(gexp, chunk);
-        ggml_tensor * k_cumdecay_chunk = get_slice_2d(k_cumdecay, chunk);
-        ggml_tensor * attn_chunk       = get_slice_2d(attn_kq, chunk);
-        cb(attn_chunk, "attn_chunk", il);
-
-        ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs);
-        cb(state_t, "state_t", il);
-
-        ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
-        cb(v_prime, "v_prime_chunk", il);
-
-        ggml_tensor * v_new   = ggml_sub(ctx0, v_prime, v_chunk);
-        ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
-        cb(v_new, "v_new_chunk", il);
-
-        ggml_tensor * q_g_exp    = ggml_mul(ctx0, q_chunk, gexp_chunk);
-        cb(q_g_exp, "q_g_exp", il);
-        ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
-        cb(attn_inter, "attn_inter_chunk", il);
-
-        ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
-        cb(v_attn, "v_attn_chunk", il);
-
-        ggml_tensor * core_attn_out_chunk = ggml_sub(ctx0, attn_inter, v_attn);
-        cb(core_attn_out_chunk, "core_attn_out_chunk", il);
-
-        core_attn_out = core_attn_out == nullptr
-            ? core_attn_out_chunk
-            : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
-
-        ggml_tensor * k_gdiff_t = get_slice_2d(key_gdiff_t, chunk);
-        ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, k_gdiff_t);
-        cb(kgdmulvnew, "kgdmulvnew", il);
-
-        ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(g_last_exp, chunk));
-        cb(gexp_last_chunk, "gexp_last_chunk", il);
-        auto s_mul = ggml_mul(ctx0, state, ggml_reshape_4d(ctx0, gexp_last_chunk, gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs));
-        cb(s_mul, "s_mul", il);
-        state = ggml_sub(ctx0, s_mul, ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
-    }
-
-    ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
-            S_v, n_tokens, H_v, n_seqs,
-            ggml_row_size(core_attn_out->type, S_v),
-            ggml_row_size(core_attn_out->type, S_v * QWEN3NEXT_CHUNK_SIZE * n_chunks),
-            ggml_row_size(core_attn_out->type, S_v * QWEN3NEXT_CHUNK_SIZE * n_chunks * H_v), 0);
-    cb(output_tokens, "output_tokens", il);
-
-    output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
-    output_tokens = ggml_cont(ctx0, output_tokens);
-    cb(output_tokens, "output_tokens", il);
-
-    return {output_tokens, state};
-}
-
-std::pair<ggml_tensor *, ggml_tensor *> delta_net::build_delta_net_autoregressive(ggml_context * ctx0,
-        ggml_tensor * q, ggml_tensor * k, ggml_tensor * v,
-        ggml_tensor * g, ggml_tensor * beta, ggml_tensor * state,
-        int il, const llm_build_cb & cb) {
-    const int64_t H_k      = q->ne[1];
-    const int64_t n_tokens = q->ne[2];
-    const int64_t n_seqs   = q->ne[3];
-
-    const int64_t S_v = v->ne[0];
-    const int64_t H_v = v->ne[1];
-
-    GGML_ASSERT(n_tokens == 1);
-    GGML_ASSERT(n_seqs == 1);
-    GGML_ASSERT(H_k == H_v);
-    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v && state->ne[2] == H_v && state->ne[3] == n_seqs);
-
-    const float scale = 1.0f / sqrtf(S_v);
-
-    q    = ggml_scale(ctx0, q, scale);
-    beta = ggml_sigmoid(ctx0, beta);
-
-    cb(q,    "q_in", il);
-    cb(k,    "k_in", il);
-    cb(v,    "v_in", il);
-    cb(beta, "beta_in", il);
-    cb(g,    "g_in", il);
-
-    ggml_tensor * g_t    = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
-    ggml_tensor * beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
-
-    g_t = ggml_exp(ctx0, g_t);
-    cb(g_t, "g_t", il);
-    state = ggml_mul(ctx0, state, g_t);
-    cb(state, "state", il);
-
-    ggml_tensor * k_t_unsqueezed = ggml_reshape_4d(ctx0, k, 1, S_v, H_v, n_seqs);
-    ggml_tensor * kv_mem         = ggml_mul(ctx0, state, k_t_unsqueezed);
-    cb(kv_mem, "kv_mem", il);
-    kv_mem = ggml_cont(ctx0, ggml_transpose(ctx0, kv_mem));
-    cb(kv_mem, "kv_mem_t_cont", il);
-    kv_mem = ggml_transpose(ctx0, ggml_sum_rows(ctx0, kv_mem));
-
-    ggml_tensor * v_t    = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
-    ggml_tensor * v_diff = ggml_sub(ctx0, v_t, kv_mem);
-    cb(v_diff, "v_diff", il);
-    ggml_tensor * delta  = ggml_mul(ctx0, v_diff, beta_t);
-    cb(delta, "delta", il);
-
-    ggml_tensor * k_t_delta = ggml_mul(ctx0, ggml_repeat_4d(ctx0, k_t_unsqueezed, S_v, S_v, H_v, n_seqs), delta);
-    cb(k_t_delta, "k_t_delta", il);
-    state                   = ggml_add(ctx0, state, k_t_delta);
-
-    ggml_tensor * q_t_unsqueezed = ggml_reshape_4d(ctx0, q, 1, S_v, H_v, n_seqs);
-    ggml_tensor * state_q        = ggml_mul(ctx0, state, q_t_unsqueezed);
-    cb(state_q, "state_q", il);
-    state_q = ggml_cont(ctx0, ggml_transpose(ctx0, state_q));
-    cb(state_q, "state_q_t_cont", il);
-    ggml_tensor * core_attn_out = ggml_transpose(ctx0, ggml_sum_rows(ctx0, state_q));
-
-    cb(core_attn_out, "output_tokens", il);
-    cb(state,         "new_state", il);
-
-    return {core_attn_out, state};
-}
-
 std::pair<ggml_tensor *, ggml_tensor *> delta_net::build_fused_delta_net(ggml_context * ctx0,
         ggml_tensor * q, ggml_tensor * k, ggml_tensor * v,
         ggml_tensor * g, ggml_tensor * beta, ggml_tensor * state,
@@ -688,14 +390,8 @@ ggml_tensor * delta_net::build_layer_attn_linear_core(ggml_context * ctx0, ggml_
     GGML_ASSERT(identity    != nullptr);
     GGML_ASSERT(diag_mask   != nullptr);
 
-    std::pair<ggml_tensor *, ggml_tensor *> attn_out;
-    // The fused delta-net implementation is only faster than chunked for n_tok <= 8, so use it only in that case
-    attn_out = n_tok <= lctx.cparams.fused_delta_net ? build_fused_delta_net(ctx0, q_conv, k_conv, v_conv, gate, beta, state, il, cb) :
-        n_tok == 1 ? build_delta_net_autoregressive(ctx0, q_conv, k_conv, v_conv, gate, beta, state, il, cb)
-                   : build_delta_net_chunking(ctx0, q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, diag_mask, il, cb);
+    auto [output, new_state] = build_fused_delta_net(ctx0, q_conv, k_conv, v_conv, gate, beta, state, il, cb);
 
-    ggml_tensor * output    = attn_out.first;
-    ggml_tensor * new_state = attn_out.second;
     cb(output, "attn_output", il);
     cb(new_state, "new_state", il);
 
diff --git a/src/llama-delta-net.h b/src/llama-delta-net.h
index e09259e5..1bd72e2c 100644
--- a/src/llama-delta-net.h
+++ b/src/llama-delta-net.h
@@ -8,17 +8,6 @@ struct delta_net {
     delta_net(llama_context & lctx, const llama_batch & batch);
     ~delta_net();
 
-    static std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_chunking(ggml_context * ctx0,
-                      ggml_tensor * q, ggml_tensor * k, ggml_tensor * v,
-                      ggml_tensor * g, ggml_tensor * beta, ggml_tensor * state,
-                      ggml_tensor * causal_mask, ggml_tensor * identity,
-                      ggml_tensor * diag_mask, int il, const llm_build_cb & cb);
-
-    static std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_autoregressive(ggml_context * ctx0,
-                      ggml_tensor * q, ggml_tensor * k, ggml_tensor * v,
-                      ggml_tensor * g, ggml_tensor * beta, ggml_tensor * state,
-                      int il, const llm_build_cb & cb);
-
     static std::pair<ggml_tensor *, ggml_tensor *> build_fused_delta_net(ggml_context * ctx0,
                       ggml_tensor * q, ggml_tensor * k, ggml_tensor * v,
                       ggml_tensor * g, ggml_tensor * beta, ggml_tensor * state,
diff --git a/src/llama.cpp b/src/llama.cpp
index facceb0d..3ccae37a 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -1512,6 +1512,7 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
         LLAMA_LOG_INFO("%s: ssm_d_inner      = %u\n",     __func__, hparams.ssm_d_inner);
         LLAMA_LOG_INFO("%s: ssm_d_state      = %u\n",     __func__, hparams.ssm_d_state);
         LLAMA_LOG_INFO("%s: ssm_dt_rank      = %u\n",     __func__, hparams.ssm_dt_rank);
+        LLAMA_LOG_INFO("%s: ssm_n_group      = %u\n",     __func__, hparams.ssm_n_group);
     }
 
     LLAMA_LOG_INFO("%s: model type       = %s\n",     __func__, llama_model_type_name(model.type));
@@ -4394,7 +4395,6 @@ struct llama_context_params llama_context_default_params() {
         /*.split_mode_graph_scheduling =*/ false,
         // /*.split_mode_f16           =*/ true,
         /*.scheduler_async             =*/ false,
-        /*.fused_delta_net             =*/ 0,
         /*.mtp                         =*/ false,
         /*.mtp_op_type                 =*/ MTP_OP_NONE,
         /*.abort_callback              =*/ nullptr,
@@ -4766,7 +4766,6 @@ struct llama_context * llama_init_from_model(
     cparams.split_mode_graph_scheduling = params.split_mode_graph_scheduling;
     //cparams.split_mode_f16   = params.split_mode_f16;
     cparams.scheduler_async  = params.scheduler_async;
-    cparams.fused_delta_net  = params.fused_delta_net;
     cparams.min_experts      = params.min_experts;
     cparams.thresh_experts   = params.thresh_experts;
     cparams.cuda_params      = params.cuda_params;
@@ -4873,7 +4872,6 @@ struct llama_context * llama_init_from_model(
     //LLAMA_LOG_INFO("%s: split_mode_f16= %d\n",     __func__, cparams.split_mode_f16);
     LLAMA_LOG_INFO("%s: reduce_type   = %s\n",     __func__, ggml_type_name(cparams.reduce_type));
     LLAMA_LOG_INFO("%s: sched_async   = %d\n",     __func__, cparams.scheduler_async);
-    LLAMA_LOG_INFO("%s: fused_delta   = %d\n",     __func__, cparams.fused_delta_net);
     LLAMA_LOG_INFO("%s: ser           = %d, %g\n", __func__, cparams.min_experts, cparams.thresh_experts);
     LLAMA_LOG_INFO("%s: freq_base     = %.1f\n",   __func__, cparams.rope_freq_base);
     LLAMA_LOG_INFO("%s: freq_scale    = %g\n",     __func__, cparams.rope_freq_scale);