diff --git a/common/arg.cpp b/common/arg.cpp
index ab23b77e0..13dfd4135 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -536,7 +536,11 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
                 throw std::invalid_argument(string_format("error: invalid argument: %s", arg.c_str()));
             }
             if (!seen_args.insert(arg).second) {
-                LOG_WRN("DEPRECATED: argument '%s' specified multiple times, use comma-separated values instead (only last value will be used)\n", arg.c_str());
+                const bool skip = (arg == "--spec-type");
+
+                if (!skip) {
+                    LOG_WRN("DEPRECATED: argument '%s' specified multiple times, use comma-separated values instead (only last value will be used)\n", arg.c_str());
+                }
             }
             auto & tmp = arg_to_options[arg];
             auto opt = *tmp.first;
@@ -893,7 +897,11 @@ bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map<com
             throw std::invalid_argument(string_format("error: invalid argument: %s", arg.c_str()));
         }
         if (!seen_args.insert(arg).second) {
-            LOG_WRN("DEPRECATED: argument '%s' specified multiple times, use comma-separated values instead (only last value will be used)\n", arg.c_str());
+            const bool skip = (arg == "--spec-type");
+
+            if (!skip) {
+                LOG_WRN("DEPRECATED: argument '%s' specified multiple times, use comma-separated values instead (only last value will be used)\n", arg.c_str());
+            }
         }
         auto opt = *arg_to_options[arg];
         std::string val;
@@ -4117,6 +4125,12 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.speculative.ngram_mod.n_match = 24;
             params.speculative.ngram_mod.n_min = 48;
             params.speculative.ngram_mod.n_max = 64;
+
+            // TODO: not sure if this is a good config - explore more settings and potentially enable it
+            //params.speculative.types.push_back(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V);
+            //params.speculative.ngram_map_k4v.size_n = 8;
+            //params.speculative.ngram_map_k4v.size_m = 24;
+            //params.speculative.ngram_map_k4v.min_hits = 2;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
 
diff --git a/common/common.cpp b/common/common.cpp
index aef06263e..b6fdec3ce 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1256,29 +1256,6 @@ common_init_result::common_init_result(common_params & params, bool model_only)
         cparams.n_samplers = pimpl->samplers_seq_config.size();
     }
 
-    // [TAG_RS_STATE_ROLLBACK_SUPPORT]
-    // TODO: ngram speculative methods require checkpointing in addition to partial RS rollback
-    //       currently this is not supported. so we disable the partial rollback
-    if (cparams.n_rs_seq > 0 && (llama_model_is_recurrent(model) || llama_model_is_hybrid(model))) {
-        auto & types = params.speculative.types;
-
-        for (int i = 0; i < (int) types.size(); i++) {
-            if (types[i] == COMMON_SPECULATIVE_TYPE_NONE) {
-                continue;
-            }
-            if (types[i] == COMMON_SPECULATIVE_TYPE_DRAFT_MTP) {
-                continue;
-            }
-
-            cparams.n_rs_seq = 0;
-
-            LOG_WRN("%s: recurrent state rollback is not compatible with '%s' - disabling rollback support\n", __func__,
-                    common_speculative_type_to_str(types[i]).c_str());
-
-            break;
-        }
-    }
-
     llama_context * lctx = llama_init_from_model(model, cparams);
     if (lctx == NULL) {
         LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str());
diff --git a/common/common.h b/common/common.h
index e03f70374..53c689bc1 100644
--- a/common/common.h
+++ b/common/common.h
@@ -299,11 +299,11 @@ struct common_params_model {
 
 // draft-model-based speculative decoding parameters
 struct common_params_speculative_draft {
-    int32_t n_max = 16; // maximum number of tokens to draft during speculative decoding
-    int32_t n_min = 0;  // minimum number of draft tokens to use for speculative decoding
+    int32_t n_max = 3; // maximum number of tokens to draft during speculative decoding
+    int32_t n_min = 0; // minimum number of draft tokens to use for speculative decoding
 
-    float p_split = 0.1f;  // speculative decoding split probability
-    float p_min   = 0.75f; // minimum speculative decoding probability (greedy) // TODO: change default to 0.0f
+    float p_split = 0.1f; // speculative decoding split probability
+    float p_min   = 0.0f; // minimum speculative decoding probability (greedy)
 
     common_params_model mparams;
 
diff --git a/common/ngram-map.cpp b/common/ngram-map.cpp
index 02bc482fe..936415976 100644
--- a/common/ngram-map.cpp
+++ b/common/ngram-map.cpp
@@ -500,7 +500,7 @@ void common_ngram_map_draft(common_ngram_map & map,
         draft.push_back(inp[match_pos + n + i]);
     }
 
-    LOG_INF("%s: key_offset = %zu, slot_max = %d, key_num = %d, draft.size = %zu\n", __func__,
+    LOG_DBG("%s: key_offset = %zu, slot_max = %d, key_num = %d, draft.size = %zu\n", __func__,
             key_offset, slot_max,
             curr_key.key_num, draft.size());
 
diff --git a/common/speculative.cpp b/common/speculative.cpp
index e591bab87..4d1b61a13 100644
--- a/common/speculative.cpp
+++ b/common/speculative.cpp
@@ -32,6 +32,19 @@ const std::map<std::string, common_speculative_type> common_speculative_type_fro
     {"ngram-cache",   COMMON_SPECULATIVE_TYPE_NGRAM_CACHE}
 };
 
+static std::string common_speculative_get_devices_str(const std::vector<ggml_backend_dev_t> & devices) {
+    if (devices.empty()) {
+        return "default";
+    }
+
+    std::string result;
+    for (size_t i = 0; i < devices.size(); i++) {
+        if (i > 0) result += ", ";
+        result += ggml_backend_dev_name(devices[i]);
+    }
+    return result;
+}
+
 struct common_speculative_config {
     common_speculative_type type;
     common_params_speculative params;
@@ -144,7 +157,7 @@ struct common_speculative_impl {
 
     virtual void draft(common_speculative_draft_params_vec & dparams) = 0;
 
-    virtual void accept(llama_seq_id seq_id, uint16_t n_accepted) = 0;
+    virtual void accept(llama_seq_id seq_id, uint16_t n_accepted, bool is_other) = 0;
 
     // true if this implementation requires the target context to extract post-norm embeddings
     virtual bool need_embd() const = 0;
@@ -167,6 +180,16 @@ struct common_speculative_impl_draft_simple : public common_speculative_impl {
         auto * ctx_dft = this->params.ctx_dft;
         auto * ctx_tgt = this->params.ctx_tgt;
 
+        LOG_INF("%s: adding speculative implementation 'draft-simple'\n", __func__);
+        LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%f\n", __func__, this->params.n_max, this->params.n_min, this->params.p_min);
+        LOG_INF("%s: - gpu_layers=%d, cache_k=%s, cache_v=%s, ctx_tgt=%s, ctx_dft=%s, devices=[%s]\n", __func__,
+                this->params.n_gpu_layers,
+                ggml_type_name(this->params.cache_type_k),
+                ggml_type_name(this->params.cache_type_v),
+                ctx_tgt ? "yes" : "no",
+                ctx_dft ? "yes" : "no",
+                common_speculative_get_devices_str(this->params.devices).c_str());
+
         batch = llama_batch_init(llama_n_batch(ctx_dft), 0, 1);
 
         // TODO: optimize or pass from outside?
@@ -343,7 +366,7 @@ struct common_speculative_impl_draft_simple : public common_speculative_impl {
         }
     }
 
-    void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/) override {
+    void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/, bool /*is_other*/) override {
         // noop
     }
 
@@ -355,8 +378,12 @@ struct common_speculative_impl_draft_simple : public common_speculative_impl {
 struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
     //common_params_speculative_eagle3 params;
 
-    common_speculative_impl_draft_eagle3(const common_params_speculative & /*params*/, uint32_t n_seq)
-        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, n_seq) {}
+    common_speculative_impl_draft_eagle3(const common_params_speculative & params, uint32_t n_seq)
+        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, n_seq)
+    {
+        LOG_INF("%s: adding speculative implementation 'draft-eagle3'\n", __func__);
+        LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%f\n", __func__, params.draft.n_max, params.draft.n_min, params.draft.p_min);
+    }
 
     void begin(llama_seq_id /*seq_id*/, const llama_tokens & /*prompt*/) override {
         // noop
@@ -371,7 +398,7 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
         // TODO: implement
     }
 
-    void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/) override {
+    void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/, bool /*is_other*/) override {
         // noop
     }
 
@@ -380,7 +407,7 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
     }
 };
 
-struct common_speculative_state_draft_mtp : public common_speculative_impl {
+struct common_speculative_impl_draft_mtp : public common_speculative_impl {
     common_params_speculative_draft params; // reuses the draft-model params slot (ctx_tgt/ctx_dft)
 
     llama_batch batch;
@@ -407,7 +434,7 @@ struct common_speculative_state_draft_mtp : public common_speculative_impl {
     // pre-advancement before process() mirrored the verify batch.
     std::vector<uint16_t> last_n_drafted;
 
-    common_speculative_state_draft_mtp(const common_params_speculative & params, uint32_t n_seq)
+    common_speculative_impl_draft_mtp(const common_params_speculative & params, uint32_t n_seq)
         : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_MTP, n_seq)
         , params(params.draft)
     {
@@ -417,6 +444,16 @@ struct common_speculative_state_draft_mtp : public common_speculative_impl {
 
         n_embd = llama_model_n_embd(llama_get_model(ctx_dft));
 
+        LOG_INF("%s: adding speculative implementation 'draft-mtp'\n", __func__);
+        LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%.2f, n_embd=%d\n", __func__, this->params.n_max, this->params.n_min, this->params.p_min, n_embd);
+        LOG_INF("%s: - gpu_layers=%d, cache_k=%s, cache_v=%s, ctx_tgt=%s, ctx_dft=%s, devices=[%s]\n", __func__,
+                this->params.n_gpu_layers,
+                ggml_type_name(this->params.cache_type_k),
+                ggml_type_name(this->params.cache_type_v),
+                ctx_tgt ? "yes" : "no",
+                ctx_dft ? "yes" : "no",
+                common_speculative_get_devices_str(this->params.devices).c_str());
+
         const int32_t n_b = (int32_t) llama_n_batch(ctx_dft);
         batch = llama_batch_init(/*n_tokens=*/ n_b, /*embd=*/ n_embd, /*n_seq_max=*/ 1);
         // llama_batch_init allocates only one of token/embd; MTP needs both.
@@ -427,7 +464,7 @@ struct common_speculative_state_draft_mtp : public common_speculative_impl {
         for (auto & s : smpls) {
             common_params_sampling sparams;
             sparams.no_perf  = false;
-            sparams.top_k    = 1; // TODO: re-enable top_k == 10 and utilize `p_min` spec param
+            sparams.top_k    = 10;
             sparams.samplers = { COMMON_SAMPLER_TYPE_TOP_K };
             s.reset(common_sampler_init(llama_get_model(ctx_dft), sparams));
         }
@@ -446,7 +483,7 @@ struct common_speculative_state_draft_mtp : public common_speculative_impl {
         last_n_drafted.assign(n_seq, 0);
     }
 
-    ~common_speculative_state_draft_mtp() override {
+    ~common_speculative_impl_draft_mtp() override {
         if (batch.token != nullptr) {
             free(batch.token);
             batch.token = nullptr;
@@ -462,7 +499,7 @@ struct common_speculative_state_draft_mtp : public common_speculative_impl {
         auto * ctx_dft = this->params.ctx_dft;
         const llama_pos pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx_dft), seq_id);
         if (pos_max < N - 1) {
-            LOG_WRN("%s: ctx_dft pos_max=%d < N-1=%d — "
+            LOG_WRN("%s: ctx_dft pos_max=%d < N-1=%d - "
                     "process() hook may not have run on every prefill ubatch "
                     "(need_embd / logits=1 on every prompt position?). "
                     "Drafts may degrade.\n",
@@ -633,6 +670,14 @@ struct common_speculative_state_draft_mtp : public common_speculative_impl {
                 // add drafted token for each sequence
                 const llama_token id = cur_p->data[0].id;
 
+                // only collect very high-confidence draft tokens
+                if (cur_p->data[0].p < params.p_min) {
+                    drafting[seq_id] = false;
+                    n_drafting--;
+
+                    continue;
+                }
+
                 common_sampler_accept(smpl, id, true);
 
                 auto & dp = dparams.at(seq_id);
@@ -678,7 +723,7 @@ struct common_speculative_state_draft_mtp : public common_speculative_impl {
         }
     }
 
-    void accept(llama_seq_id seq_id, uint16_t n_accepted) override {
+    void accept(llama_seq_id seq_id, uint16_t n_accepted, bool /*is_other*/) override {
         if (seq_id < 0 || seq_id >= (llama_seq_id) n_seq) {
             return;
         }
@@ -714,7 +759,12 @@ struct common_speculative_impl_ngram_simple : public common_speculative_impl {
             common_ngram_simple_config config)
         : common_speculative_impl(COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE, n_seq)
         , params(params.ngram_simple)
-        , config(config) {}
+        , config(config)
+    {
+        LOG_INF("%s: adding speculative implementation 'ngram-simple'\n", __func__);
+        LOG_INF("%s: - size_n=%d, size_m=%d, min_hits=%d\n", __func__,
+                this->params.size_n, this->params.size_m, this->params.min_hits);
+    }
 
     void begin(llama_seq_id /*seq_id*/, const llama_tokens & /*prompt*/) override {
         // noop
@@ -738,7 +788,7 @@ struct common_speculative_impl_ngram_simple : public common_speculative_impl {
         }
     }
 
-    void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/) override {
+    void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/, bool /*is_other*/) override {
         // noop
     }
 
@@ -748,20 +798,21 @@ struct common_speculative_impl_ngram_simple : public common_speculative_impl {
 };
 
 struct common_speculative_impl_ngram_map_k : public common_speculative_impl {
-    common_params_speculative_ngram_map params;
-
     // n_seq configs
     std::vector<common_ngram_map> config;
 
     common_speculative_impl_ngram_map_k(
-            const common_params_speculative & params,
             const common_ngram_map & config,
             uint32_t n_seq)
         : common_speculative_impl(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K, n_seq)
-        , params(params.ngram_map_k) {
+    {
         for (uint32_t i = 0; i < n_seq; i++) {
             this->config.push_back(config);
         }
+
+        LOG_INF("%s: adding speculative implementation '%s'\n", __func__, common_speculative_type_to_str(this->type).c_str());
+        LOG_INF("%s: - size_key=%d, size_value=%d, key_only=%d, min_hits=%d\n", __func__,
+                config.size_key, config.size_value, config.key_only, config.min_hits);
     }
 
     void begin(llama_seq_id seq_id, const llama_tokens & prompt) override {
@@ -788,9 +839,13 @@ struct common_speculative_impl_ngram_map_k : public common_speculative_impl {
         }
     }
 
-    void accept(llama_seq_id seq_id, uint16_t n_accepted) override {
+    void accept(llama_seq_id seq_id, uint16_t n_accepted, bool is_other) override {
         GGML_ASSERT((seq_id < (llama_seq_id) config.size()));
 
+        if (is_other) {
+            return;
+        }
+
         common_ngram_map_accept(config[seq_id], n_accepted);
     }
 
@@ -812,7 +867,7 @@ struct common_speculative_impl_ngram_mod : public common_speculative_impl {
         // the last position in the prompt that was added to the ngram container
         size_t i_last = 0;
 
-        // length of the last drafted n‑gram (number of tokens returned by draft)
+        // length of the last drafted n-gram (number of tokens returned by draft)
         size_t n_draft_last = 0;
 
         // consecutive accept rounds with low acceptance fraction (< 0.5)
@@ -830,8 +885,11 @@ struct common_speculative_impl_ngram_mod : public common_speculative_impl {
         , verbose(std::getenv("LLAMA_TRACE") != nullptr) {
         static_assert(sizeof(llama_token) == sizeof(common_ngram_mod::entry_t));
 
-        LOG_INF("%s: initialized ngram_mod with n_match=%d, size=%zu (%.3f MB)\n", __func__,
-                this->params.n_match, mod.size(), (float)(mod.size_bytes())/1024/1024);
+        LOG_INF("%s: adding speculative implementation 'ngram-mod'\n", __func__);
+        LOG_INF("%s: - n_match=%d, n_max=%d, n_min=%d\n", __func__,
+                this->params.n_match, this->params.n_max, this->params.n_min);
+        LOG_INF("%s: - mod size=%zu (%.3f MB)\n", __func__,
+                mod.size(), (float)(mod.size_bytes())/1024/1024);
 
         if (this->params.n_match < 16) {
             LOG_WRN("%s: ngram_mod n_match=%d is too small - poor quality is possible, "
@@ -921,7 +979,7 @@ struct common_speculative_impl_ngram_mod : public common_speculative_impl {
         }
         result.resize(result.size() - n);
 
-        // store length of drafted n‑gram for later acceptance analysis
+        // store length of drafted n-gram for later acceptance analysis
         sinfo.n_draft_last = result.size();
     }
 
@@ -943,17 +1001,21 @@ struct common_speculative_impl_ngram_mod : public common_speculative_impl {
         }
     }
 
-    void accept(llama_seq_id seq_id, uint16_t n_accepted) override {
+    void accept(llama_seq_id seq_id, uint16_t n_accepted, bool is_other) override {
+        if (is_other) {
+            return;
+        }
+
         auto & sinfo = sinfos[seq_id];
 
         // compute acceptance fraction if we have a recorded draft length
         if (sinfo.n_draft_last > 0) {
             const double f_acc = (double)n_accepted / (double)sinfo.n_draft_last;
-            if (f_acc < 0.5) {
+            if (f_acc < 0.25) {
                 sinfo.n_low++;
-                if (sinfo.n_low >= 3) {
+                if (sinfo.n_low >= 5) {
                     if (verbose) {
-                        LOG_WRN("%s: low acceptance streak (%d) – resetting ngram_mod\n", __func__, sinfo.n_low);
+                        LOG_WRN("%s: low acceptance streak (%d) - resetting ngram_mod\n", __func__, sinfo.n_low);
                     }
 
                     mod.reset();
@@ -1003,6 +1065,12 @@ struct common_speculative_impl_ngram_cache : public common_speculative_impl {
         , save_dynamic(save_dynamic)
         , save_static(save_static)
     {
+        LOG_INF("%s: adding speculative implementation 'ngram-cache'\n", __func__);
+        LOG_INF("%s: - n_draft=%d, cache_static=%s, cache_dynamic=%s\n", __func__,
+                n_draft,
+                path_static.empty() ? "none" : path_static.c_str(),
+                path_dynamic.empty() ? "none" : path_dynamic.c_str());
+
         sinfos.resize(n_seq);
 
         if (!path_static.empty()) {
@@ -1099,7 +1167,7 @@ struct common_speculative_impl_ngram_cache : public common_speculative_impl {
         }
     }
 
-    void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/) override {
+    void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/, bool /*is_other*/) override {
         // noop
     }
 
@@ -1285,7 +1353,6 @@ common_speculative * common_speculative_init(common_params_speculative & params,
     std::vector<std::unique_ptr<common_speculative_impl>> impls = {};
 
     for (const common_speculative_config & config : configs) {
-        LOG_INF("%s: adding speculative implementation '%s'\n", __func__, common_speculative_type_to_str(config.type).c_str());
         switch (config.type) {
             case COMMON_SPECULATIVE_TYPE_NONE:
                 break;
@@ -1298,7 +1365,7 @@ common_speculative * common_speculative_init(common_params_speculative & params,
                 break;
             }
             case COMMON_SPECULATIVE_TYPE_DRAFT_MTP: {
-                impls.push_back(std::make_unique<common_speculative_state_draft_mtp>(config.params, n_seq));
+                impls.push_back(std::make_unique<common_speculative_impl_draft_mtp>(config.params, n_seq));
                 break;
             }
             case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE: {
@@ -1319,11 +1386,16 @@ common_speculative * common_speculative_init(common_params_speculative & params,
                 impls.push_back(std::move(state));
                 break;
             }
-            case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K:
+            case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K: {
+                impls.push_back(
+                        std::make_unique<common_speculative_impl_ngram_map_k>(
+                            get_common_ngram_map(config.type, config.params.ngram_map_k), n_seq));
+                break;
+            }
             case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V: {
                 impls.push_back(
                         std::make_unique<common_speculative_impl_ngram_map_k>(
-                            config.params, get_common_ngram_map(config.type, config.params.ngram_map_k), n_seq));
+                            get_common_ngram_map(config.type, config.params.ngram_map_k4v), n_seq));
                 break;
             }
             case COMMON_SPECULATIVE_TYPE_NGRAM_MOD: {
@@ -1515,11 +1587,6 @@ void common_speculative_accept(common_speculative * spec, llama_seq_id seq_id, u
 
     GGML_ASSERT(impl);
 
-    // TODO: currently only the implementation that generated the draft is used to accept it
-    //       however, some implementations (such as MTP) need to also "see" the accepted tokens
-    //       extend `common_speculative_impl::accept()` with an extra argument `bool is_other` to
-    //       inform the implementation if the accepted tokens are from another implementation and
-    //       pass the accepted tokens to all remaining implementations using `is_other == true`
     {
         common_time_meas tm(impl->t_accept_us, !impl->gen_perf);
         if (n_accepted > 0) {
@@ -1527,9 +1594,16 @@ void common_speculative_accept(common_speculative * spec, llama_seq_id seq_id, u
             impl->n_acc_tokens += n_accepted;
         }
 
-        impl->accept(seq_id, n_accepted);
+        impl->accept(seq_id, n_accepted, false);
         impl->n_call_accept++;
     }
+
+    // accept with the rest of the implementations, using is_other == true
+    for (auto & impl_other : spec->impls) {
+        if (impl_other.get() != impl) {
+            impl_other->accept(seq_id, n_accepted, true);
+        }
+    }
 }
 
 void common_speculative_print_stats(const common_speculative * spec) {
@@ -1549,7 +1623,7 @@ void common_speculative_print_stats(const common_speculative * spec) {
             str_perf = "";
         }
 
-        LOG_INF("statistics %s: #calls(b,g,a) = %zu %zu %zu, #gen drafts = %zu, #acc drafts = %zu, #gen tokens = %zu, #acc tokens = %zu%s\n",
+        LOG_INF("statistics %16s: #calls(b,g,a) = %4zu %6zu %6zu, #gen drafts = %6zu, #acc drafts = %5zu, #gen tokens = %6zu, #acc tokens = %5zu%s\n",
                 common_speculative_type_to_str(impl->type).c_str(),
                 impl->n_call_begin, impl->n_call_draft, impl->n_call_accept,
                 impl->n_gen_drafts,
diff --git a/docs/speculative.md b/docs/speculative.md
index fb6ef0306..45e42d42a 100644
--- a/docs/speculative.md
+++ b/docs/speculative.md
@@ -108,11 +108,12 @@ If a draft model is combined with a draftless decoding the draftless decoding ha
 ### General Speculative Parameters
 
 ```
---spec-type [none|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod]
-                                        type of speculative decoding to use when no draft model is provided
+--spec-type [none|draft-simple|draft-mtp|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod]
+                                        comma-separated list of types of speculative decoding to use
                                         (default: none)
                                         (env: LLAMA_ARG_SPEC_TYPE)
---spec-default                          use default speculative decoding
+--spec-default                          use default speculative decoding config
+                                        (enables ngram-mod)
 ```
 
 ### Draft Model Parameters
@@ -123,8 +124,9 @@ If a draft model is combined with a draftless decoding the draftless decoding ha
                                         (env: LLAMA_ARG_SPEC_DRAFT_MODEL)
 --spec-draft-hf, -hfd, -hfrd, --hf-repo-draft  <user>/<model>[:quant]
                                         HuggingFace repository for the draft model
+                                        (env: LLAMA_ARG_SPEC_DRAFT_HF_REPO)
 --spec-draft-n-max                      N
-                                        number of tokens to draft for speculative decoding (default: 16)
+                                        number of tokens to draft for speculative decoding (default: 3)
                                         (env: LLAMA_ARG_SPEC_DRAFT_N_MAX)
 --spec-draft-n-min                      N
                                         minimum number of draft tokens to use for speculative decoding (default: 0)
@@ -133,18 +135,64 @@ If a draft model is combined with a draftless decoding the draftless decoding ha
                                         speculative decoding split probability (default: 0.10)
                                         (env: LLAMA_ARG_SPEC_DRAFT_P_SPLIT)
 --spec-draft-p-min, --draft-p-min       P
-                                        minimum speculative decoding probability (greedy) (default: 0.75)
+                                        minimum speculative decoding probability (greedy) (default: 0.00)
                                         (env: LLAMA_ARG_SPEC_DRAFT_P_MIN)
---spec-draft-ctx-size, -cd, --ctx-size-draft  N
-                                        size of the prompt context for the draft model (default: 0, 0 = loaded from model)
-                                        (env: LLAMA_ARG_SPEC_DRAFT_CTX_SIZE)
 --spec-draft-ngl, -ngld, --gpu-layers-draft, --n-gpu-layers-draft  N
                                         max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)
                                         (env: LLAMA_ARG_N_GPU_LAYERS_DRAFT)
 --spec-draft-device, -devd, --device-draft  <dev1,dev2,..>
                                         comma-separated list of devices to use for offloading the draft model
---spec-draft-replace, --spec-replace    TARGET  DRAFT
-                                        translate the string in TARGET into DRAFT if the draft model and main model are not compatible
+                                        (use --list-devices to see available devices)
+```
+
+### Draft Model CPU Scheduling Parameters
+
+```
+--spec-draft-threads, -td, --threads-draft  N
+                                        number of CPU threads to use during generation
+--spec-draft-threads-batch, -tbd, --threads-batch-draft  N
+                                        number of threads to use during batch and prompt processing (default: same as --threads-draft)
+--spec-draft-cpu-mask, -Cd, --cpu-mask-draft  M
+                                        Draft model CPU affinity mask. Complements cpu-range-draft
+--spec-draft-cpu-range, -Crd, --cpu-range-draft  lo-hi
+                                        Ranges of CPUs for affinity. Complements --cpu-mask-draft
+--spec-draft-cpu-strict, --cpu-strict-draft  <0|1>
+                                        Use strict CPU placement for draft model (default: same as --cpu-strict)
+--spec-draft-prio, --prio-draft  N
+                                        set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime
+--spec-draft-poll, --poll-draft  <0|1>
+                                        Use polling to wait for draft model work (default: same as --poll)
+--spec-draft-cpu-mask-batch, -Cbd, --cpu-mask-batch-draft  M
+                                        Draft model CPU affinity mask for batch. Complements cpu-range-batch-draft
+--spec-draft-cpu-range-batch, -Crbd, --cpu-range-batch-draft  lo-hi
+                                        Ranges of CPUs for affinity for batch. Complements --cpu-mask-batch-draft
+--spec-draft-cpu-strict-batch, --cpu-strict-batch-draft  <0|1>
+                                        Use strict CPU placement for draft model batch (default: --cpu-strict-draft)
+--spec-draft-prio-batch, --prio-batch-draft  N
+                                        set draft process/thread priority for batch : 0-normal, 1-medium, 2-high, 3-realtime
+--spec-draft-poll-batch, --poll-batch-draft  <0|1>
+                                        Use polling to wait for draft model work for batch (default: --poll-draft)
+```
+
+### Draft Model KV Cache and Tensor Override Parameters
+
+```
+--spec-draft-type-k, -ctkd, --cache-type-k-draft  TYPE
+                                        KV cache data type for K for the draft model
+                                        allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
+                                        (env: LLAMA_ARG_SPEC_DRAFT_CACHE_TYPE_K)
+--spec-draft-type-v, -ctvd, --cache-type-v-draft  TYPE
+                                        KV cache data type for V for the draft model
+                                        allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
+                                        (env: LLAMA_ARG_SPEC_DRAFT_CACHE_TYPE_V)
+--spec-draft-override-tensor, -otd, --override-tensor-draft  <tensor name pattern>=<buffer type>,...
+                                        override tensor buffer type for draft model
+--spec-draft-cpu-moe, -cmoed, --cpu-moe-draft
+                                        keep all Mixture of Experts (MoE) weights in the CPU for the draft model
+                                        (env: LLAMA_ARG_SPEC_DRAFT_CPU_MOE)
+--spec-draft-n-cpu-moe, --spec-draft-ncmoe, -ncmoed, --n-cpu-moe-draft  N
+                                        keep the MoE weights of the first N layers in the CPU for the draft model
+                                        (env: LLAMA_ARG_SPEC_DRAFT_N_CPU_MOE)
 ```
 
 ### n-gram Mod Parameters
@@ -193,11 +241,13 @@ If a draft model is combined with a draftless decoding the draftless decoding ha
 
 ### `--spec-type TYPE`
 
-Specifies a type of speculative decoding without draft model.
+Specifies a comma-separated list of speculative decoding types to use.
 
 | Type | Description |
 |------|-------------|
 | `none` | No speculative decoding (default) |
+| `draft-simple` | Use a simple draft model for speculation |
+| `draft-mtp` | Use Masked Token Prediction (MTP) heads from the main model |
 | `ngram-cache` | Use n-gram cache lookup |
 | `ngram-simple` | Use simple n-gram pattern matching |
 | `ngram-map-k` | Use n-gram pattern matching with n-gram-keys |
@@ -209,6 +259,11 @@ Specifies a type of speculative decoding without draft model.
 ./llama-server [...] --spec-type ngram-simple
 ```
 
+**Example:** Multiple speculative implementations.
+```bash
+./llama-server [...] --spec-type ngram-mod,ngram-map-k4v
+```
+
 ### `--spec-ngram-*-size-n N`
 
 Sets the size N of the lookup n-gram for n-gram map based speculative decoding.
diff --git a/src/llama-graph.h b/src/llama-graph.h
index 9e55d0a67..bf6778237 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -581,7 +581,8 @@ struct llm_graph_params {
             ubatch.n_seqs_unq   == other.ubatch.n_seqs_unq &&
             (
                 (!ubatch.token && !other.ubatch.token) ||
-                (!ubatch.embd  && !other.ubatch.embd)
+                (!ubatch.embd  && !other.ubatch.embd)  ||
+                (ubatch.token && other.ubatch.token && ubatch.embd && other.ubatch.embd)
             );
 
         // when we split the batch using "equal_seqs" we have to verify that the participating sequences are the same
diff --git a/src/llama-memory-hybrid-iswa.cpp b/src/llama-memory-hybrid-iswa.cpp
index a59561ea5..72f5c2fea 100644
--- a/src/llama-memory-hybrid-iswa.cpp
+++ b/src/llama-memory-hybrid-iswa.cpp
@@ -75,9 +75,15 @@ llama_memory_context_ptr llama_memory_hybrid_iswa::init_batch(llama_batch_allocr
                 // if all tokens are output, split by sequence
                 ubatch = balloc.split_seq(n_ubatch);
             } else {
-                // Use non-sequential split when KV cache is unified (needed for hellaswag/winogrande/multiple-choice)
-                const bool unified = (mem_attn->get_base()->get_n_stream() == 1);
-                ubatch = balloc.split_equal(n_ubatch, !unified);
+                if (mem_recr->n_rs_seq > 0) {
+                    // [TAG_RECURRENT_ROLLBACK_SPLITS]
+                    // TODO: recurrent state rollback does not support equal splits
+                    ubatch = balloc.split_seq(n_ubatch);
+                } else {
+                    // Use non-sequential split when KV cache is unified (needed for hellaswag/winogrande/multiple-choice)
+                    const bool unified = (mem_attn->get_base()->get_n_stream() == 1);
+                    ubatch = balloc.split_equal(n_ubatch, !unified);
+                }
             }
 
             if (ubatch.n_tokens == 0) {
diff --git a/src/llama-memory-hybrid.cpp b/src/llama-memory-hybrid.cpp
index fd305cab7..33b3b395e 100644
--- a/src/llama-memory-hybrid.cpp
+++ b/src/llama-memory-hybrid.cpp
@@ -75,9 +75,15 @@ llama_memory_context_ptr llama_memory_hybrid::init_batch(llama_batch_allocr & ba
                 // if all tokens are output, split by sequence
                 ubatch = balloc.split_seq(n_ubatch);
             } else {
-                // Use non-sequential split when KV cache is unified (needed for hellaswag/winogrande/multiple-choice)
-                const bool unified = (mem_attn->get_n_stream() == 1);
-                ubatch = balloc.split_equal(n_ubatch, !unified);
+                if (mem_recr->n_rs_seq > 0) {
+                    // [TAG_RECURRENT_ROLLBACK_SPLITS]
+                    // TODO: recurrent state rollback does not support equal splits
+                    ubatch = balloc.split_seq(n_ubatch);
+                } else {
+                    // Use non-sequential split when KV cache is unified (needed for hellaswag/winogrande/multiple-choice)
+                    const bool unified = (mem_attn->get_n_stream() == 1);
+                    ubatch = balloc.split_equal(n_ubatch, !unified);
+                }
             }
 
             if (ubatch.n_tokens == 0) {
diff --git a/src/llama-memory-recurrent.cpp b/src/llama-memory-recurrent.cpp
index aeb866657..ec5dc5835 100644
--- a/src/llama-memory-recurrent.cpp
+++ b/src/llama-memory-recurrent.cpp
@@ -416,9 +416,15 @@ llama_memory_context_ptr llama_memory_recurrent::init_batch(llama_batch_allocr &
                 // if all tokens are output, split by sequence
                 ubatch = balloc.split_seq(n_ubatch);
             } else {
-                // TODO: non-sequential equal split can be done if using unified KV cache
-                //       for simplicity, we always use sequential equal split for now
-                ubatch = balloc.split_equal(n_ubatch, true);
+                if (n_rs_seq > 0) {
+                    // [TAG_RECURRENT_ROLLBACK_SPLITS]
+                    // TODO: recurrent state rollback does not support equal splits
+                    ubatch = balloc.split_seq(n_ubatch);
+                } else {
+                    // TODO: non-sequential equal split can be done if using unified KV cache
+                    //       for simplicity, we always use sequential equal split for now
+                    ubatch = balloc.split_equal(n_ubatch, true);
+                }
             }
 
             if (ubatch.n_tokens == 0) {
diff --git a/src/llama-memory-recurrent.h b/src/llama-memory-recurrent.h
index 29c58afc9..b13b7b748 100644
--- a/src/llama-memory-recurrent.h
+++ b/src/llama-memory-recurrent.h
@@ -72,6 +72,7 @@ public:
 
     // number of recurrent-state snapshots per seq for rollback; tensors are widened to (1 + n_rs_seq) groups
     uint32_t n_rs_seq = 0;
+
     // per-seq rollback index
     std::vector<uint32_t> rs_idx;
 
diff --git a/src/models/delta-net-base.cpp b/src/models/delta-net-base.cpp
index 2a4e00384..a67238383 100644
--- a/src/models/delta-net-base.cpp
+++ b/src/models/delta-net-base.cpp
@@ -447,13 +447,6 @@ std::pair<ggml_tensor *, ggml_tensor *> llm_build_delta_net_base::build_delta_ne
     return build_delta_net_chunking(q, k, v, g, b, s, il);
 }
 
-bool llm_build_delta_net_base::keep_rs() const {
-    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
-    return cparams.n_rs_seq > 0
-        && n_seq_tokens > 1
-        && (uint32_t) n_seq_tokens <= 1 + cparams.n_rs_seq;
-}
-
 ggml_tensor * llm_build_delta_net_base::build_conv_state(
         llm_graph_input_rs * inp,
         ggml_tensor *        conv_states_all,
@@ -461,12 +454,12 @@ ggml_tensor * llm_build_delta_net_base::build_conv_state(
         int64_t              conv_kernel_size,
         int64_t              conv_channels,
         int                  il) {
-    const auto * mctx_cur   = inp->mctx;
-    const auto   kv_head    = mctx_cur->get_head();
-    const uint32_t mem_size = mctx_cur->get_size();
-    const int64_t n_seqs       = ubatch.n_seqs;
-    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
-    const bool    keep         = keep_rs();
+    const auto * mctx_cur = inp->mctx;
+
+    const auto kv_head  = mctx_cur->get_head();
+    const auto mem_size = mctx_cur->get_size();
+
+    const int64_t n_seqs = ubatch.n_seqs;
 
     ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
     cb(conv_states, "conv_states", il);
@@ -480,32 +473,52 @@ ggml_tensor * llm_build_delta_net_base::build_conv_state(
     ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0);
     cb(conv_input, "conv_input", il);
 
-    if (!keep) {
-        ggml_tensor * last_conv_states =
-            ggml_view_3d(ctx0, conv_input, conv_kernel_size - 1, conv_channels, n_seqs, conv_input->nb[1],
-                         conv_input->nb[2], (conv_input->ne[0] - conv_states->ne[0]) * ggml_element_size(conv_input));
-        cb(last_conv_states, "last_conv_states", il);
+    const int64_t row_count = (conv_kernel_size - 1) * conv_channels;
 
-        ggml_tensor * state_update_target =
-            ggml_view_2d(ctx0, conv_states_all, (conv_kernel_size - 1) * conv_channels, n_seqs, conv_states_all->nb[1],
-                         kv_head * (conv_kernel_size - 1) * conv_channels * ggml_element_size(conv_states_all));
-        cb(state_update_target, "state_update_target", il);
+    const size_t row_size  = ggml_row_size(conv_states_all->type, row_count);
 
-        ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target));
+    if (cparams.n_rs_seq == 0) {
+        const int64_t s_idx  = conv_input->ne[0] - conv_states->ne[0];
+        const int64_t s_slot = 0;
+
+        ggml_tensor * conv_state_last =
+            ggml_view_3d(ctx0, conv_input,
+                    conv_kernel_size - 1, conv_channels, n_seqs,
+                    conv_input->nb[1], conv_input->nb[2],
+                    ggml_row_size(conv_input->type, s_idx));
+        cb(conv_state_last, "conv_state_last", il);
+
+        ggml_tensor * conv_state_update =
+            ggml_view_2d(ctx0, conv_states_all,
+                    row_count, n_seqs, conv_states_all->nb[1],
+                    (s_slot * mem_size + kv_head) * row_size);
+        cb(conv_state_update, "conv_state_update", il);
+
+        ggml_build_forward_expand(gf, ggml_cpy(ctx0, conv_state_last, conv_state_update));
     } else {
-        const int64_t row_count = (conv_kernel_size - 1) * conv_channels;
-        const size_t  row_size  = row_count * ggml_element_size(conv_states_all);
-        for (int64_t t = 1; t <= n_seq_tokens; ++t) {
-            const uint32_t slot = (uint32_t)(n_seq_tokens - t);
-            ggml_tensor * src =
-                ggml_view_3d(ctx0, conv_input, conv_kernel_size - 1, conv_channels, n_seqs,
-                             conv_input->nb[1], conv_input->nb[2],
-                             t * ggml_element_size(conv_input));
-            ggml_tensor * dst =
-                ggml_view_2d(ctx0, conv_states_all, row_count, n_seqs,
-                             conv_states_all->nb[1],
-                             ((size_t) slot * mem_size + kv_head) * row_size);
-            ggml_build_forward_expand(gf, ggml_cpy(ctx0, src, dst));
+        // [TAG_RECURRENT_ROLLBACK_SPLITS]
+        // TODO: this logic incorrectly assumes that the last (n_rs_seq + 1) tokens of a sequence in a batch are
+        //       inside the same ubatch. currently with `split_equal()` this is not correct
+
+        const int64_t K = (int64_t) cparams.n_rs_seq + 1;
+
+        for (int64_t t = 1; t <= K; ++t) {
+            const int64_t s_idx  = std::max<int64_t>(0, conv_input->ne[0] - conv_states->ne[0] - K + t);
+            const int64_t s_slot = K - t;
+
+            ggml_tensor * conv_state_last =
+                ggml_view_3d(ctx0, conv_input,
+                        conv_kernel_size - 1, conv_channels, n_seqs,
+                        conv_input->nb[1], conv_input->nb[2],
+                        ggml_row_size(conv_input->type, s_idx));
+
+            ggml_tensor * conv_state_update =
+                ggml_view_2d(ctx0,
+                        conv_states_all, row_count, n_seqs,
+                        conv_states_all->nb[1],
+                        (s_slot * mem_size + kv_head) * row_size);
+
+            ggml_build_forward_expand(gf, ggml_cpy(ctx0, conv_state_last, conv_state_update));
         }
     }
 
@@ -531,7 +544,9 @@ ggml_tensor * llm_build_delta_net_base::build_recurrent_attn(
     const int64_t n_seqs       = s->ne[3];
     const int64_t n_seq_tokens = q->ne[2];
 
-    if (!keep_rs()) {
+    const bool keep = cparams.n_rs_seq > 0;
+
+    if (!keep) {
         auto attn_out = build_delta_net(q, k, v, g, b, s, il);
         ggml_tensor * output    = attn_out.first;
         ggml_tensor * new_state = attn_out.second;
@@ -554,7 +569,11 @@ ggml_tensor * llm_build_delta_net_base::build_recurrent_attn(
     ggml_tensor * state_3d    = ggml_pad(ctx0, state_in_3d, 0, K - 1, 0, 0);
 
     ggml_tensor * gdn_out = ggml_gated_delta_net(ctx0, q, k, v, g, b, state_3d);
-    cb(gdn_out, LLAMA_TENSOR_NAME_FGDN_CH, il);
+    if (n_seq_tokens > 1) {
+        cb(gdn_out, LLAMA_TENSOR_NAME_FGDN_CH, il);
+    } else {
+        cb(gdn_out, LLAMA_TENSOR_NAME_FGDN_AR, il);
+    }
 
     const int64_t attn_score_elems    = S_v * H_v * n_seq_tokens * n_seqs;
     const int64_t state_size_per_snap = S_v * S_v * H_v * n_seqs;
@@ -576,9 +595,11 @@ ggml_tensor * llm_build_delta_net_base::build_recurrent_attn(
             ggml_row_size(gdn_out->type, S_v * S_v),
             ggml_row_size(gdn_out->type, S_v * S_v * H_v),
             ggml_row_size(gdn_out->type, attn_score_elems + k_i * state_size_per_snap));
+
         ggml_tensor * dst = ggml_view_2d(ctx0, ssm_states_all,
             hparams.n_embd_s(), n_seqs, ssm_states_all->nb[1],
             ((size_t) cache_slot * mem_size + kv_head) * row_size);
+
         ggml_build_forward_expand(gf, ggml_cpy(ctx0, src, dst));
     }
 
diff --git a/src/models/models.h b/src/models/models.h
index 4e40536a5..7e551eb96 100644
--- a/src/models/models.h
+++ b/src/models/models.h
@@ -66,9 +66,6 @@ struct llm_build_delta_net_base : public llm_graph_context {
                 ggml_tensor * s,
                         int   il);
 
-    // true when speculative rollback is enabled and the batch fits in the rs cache
-    bool keep_rs() const;
-
     // read conv state from cache, concat with qkv_mixed, write back (single slot or per-token)
     // qkv_mixed: (qkv_dim, n_seq_tokens, n_seqs); returns conv_input: (kernel_size + n_seq_tokens - 1, channels, n_seqs)
     ggml_tensor * build_conv_state(
diff --git a/tools/cli/README.md b/tools/cli/README.md
index c40b5a21c..38bc78a3f 100644
--- a/tools/cli/README.md
+++ b/tools/cli/README.md
@@ -191,10 +191,10 @@
 | `--spec-draft-override-tensor, -otd, --override-tensor-draft <tensor name pattern>=<buffer type>,...` | override tensor buffer type for draft model |
 | `--spec-draft-cpu-moe, -cmoed, --cpu-moe-draft` | keep all Mixture of Experts (MoE) weights in the CPU for the draft model<br/>(env: LLAMA_ARG_SPEC_DRAFT_CPU_MOE) |
 | `--spec-draft-n-cpu-moe, --spec-draft-ncmoe, -ncmoed, --n-cpu-moe-draft N` | keep the Mixture of Experts (MoE) weights of the first N layers in the CPU for the draft model<br/>(env: LLAMA_ARG_SPEC_DRAFT_N_CPU_MOE) |
-| `--spec-draft-n-max N` | number of tokens to draft for speculative decoding (default: 16)<br/>(env: LLAMA_ARG_SPEC_DRAFT_N_MAX) |
+| `--spec-draft-n-max N` | number of tokens to draft for speculative decoding (default: 3)<br/>(env: LLAMA_ARG_SPEC_DRAFT_N_MAX) |
 | `--spec-draft-n-min N` | minimum number of draft tokens to use for speculative decoding (default: 0)<br/>(env: LLAMA_ARG_SPEC_DRAFT_N_MIN) |
 | `--spec-draft-p-split, --draft-p-split P` | speculative decoding split probability (default: 0.10)<br/>(env: LLAMA_ARG_SPEC_DRAFT_P_SPLIT) |
-| `--spec-draft-p-min, --draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.75)<br/>(env: LLAMA_ARG_SPEC_DRAFT_P_MIN) |
+| `--spec-draft-p-min, --draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.00)<br/>(env: LLAMA_ARG_SPEC_DRAFT_P_MIN) |
 | `--spec-draft-device, -devd, --device-draft <dev1,dev2,..>` | comma-separated list of devices to use for offloading the draft model (none = don't offload)<br/>use --list-devices to see a list of available devices |
 | `--spec-draft-ngl, -ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)<br/>(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) |
 | `--spec-draft-model, -md, --model-draft FNAME` | draft model for speculative decoding (default: unused)<br/>(env: LLAMA_ARG_SPEC_DRAFT_MODEL) |
diff --git a/tools/server/README.md b/tools/server/README.md
index 11098af28..9b4134239 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -183,6 +183,7 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `--image-max-tokens N` | maximum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)<br/>(env: LLAMA_ARG_IMAGE_MAX_TOKENS) |
 | `-a, --alias STRING` | set model name aliases, comma-separated (to be used by API)<br/>(env: LLAMA_ARG_ALIAS) |
 | `--tags STRING` | set model tags, comma-separated (informational, not used for routing)<br/>(env: LLAMA_ARG_TAGS) |
+| `--embd-normalize N` | normalisation for embeddings (default: 2) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm) |
 | `--host HOST` | ip address to listen, or bind to an UNIX socket if the address ends with .sock (default: 127.0.0.1)<br/>(env: LLAMA_ARG_HOST) |
 | `--port PORT` | port to listen (default: 8080)<br/>(env: LLAMA_ARG_PORT) |
 | `--reuse-port` | allow multiple sockets to bind to the same port (default: disabled)<br/>(env: LLAMA_ARG_REUSE_PORT) |
@@ -244,10 +245,10 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `--spec-draft-override-tensor, -otd, --override-tensor-draft <tensor name pattern>=<buffer type>,...` | override tensor buffer type for draft model |
 | `--spec-draft-cpu-moe, -cmoed, --cpu-moe-draft` | keep all Mixture of Experts (MoE) weights in the CPU for the draft model<br/>(env: LLAMA_ARG_SPEC_DRAFT_CPU_MOE) |
 | `--spec-draft-n-cpu-moe, --spec-draft-ncmoe, -ncmoed, --n-cpu-moe-draft N` | keep the Mixture of Experts (MoE) weights of the first N layers in the CPU for the draft model<br/>(env: LLAMA_ARG_SPEC_DRAFT_N_CPU_MOE) |
-| `--spec-draft-n-max N` | number of tokens to draft for speculative decoding (default: 16)<br/>(env: LLAMA_ARG_SPEC_DRAFT_N_MAX) |
+| `--spec-draft-n-max N` | number of tokens to draft for speculative decoding (default: 3)<br/>(env: LLAMA_ARG_SPEC_DRAFT_N_MAX) |
 | `--spec-draft-n-min N` | minimum number of draft tokens to use for speculative decoding (default: 0)<br/>(env: LLAMA_ARG_SPEC_DRAFT_N_MIN) |
 | `--spec-draft-p-split, --draft-p-split P` | speculative decoding split probability (default: 0.10)<br/>(env: LLAMA_ARG_SPEC_DRAFT_P_SPLIT) |
-| `--spec-draft-p-min, --draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.75)<br/>(env: LLAMA_ARG_SPEC_DRAFT_P_MIN) |
+| `--spec-draft-p-min, --draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.00)<br/>(env: LLAMA_ARG_SPEC_DRAFT_P_MIN) |
 | `--spec-draft-device, -devd, --device-draft <dev1,dev2,..>` | comma-separated list of devices to use for offloading the draft model (none = don't offload)<br/>use --list-devices to see a list of available devices |
 | `--spec-draft-ngl, -ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)<br/>(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) |
 | `--spec-draft-model, -md, --model-draft FNAME` | draft model for speculative decoding (default: unused)<br/>(env: LLAMA_ARG_SPEC_DRAFT_MODEL) |