diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 8f318c4a4c..473ff9296e 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -191,6 +191,11 @@ struct server_slot {
         n_draft_total = 0;
         n_draft_accepted = 0;
 
+        spec_n_denials = 0;
+        spec_n_accepted = 0;
+        spec_has_ckpt = false;
+        spec_ckpt_size_part = 0;
+
         task_prev = std::move(task);
         task.reset();
 
@@ -2049,7 +2054,8 @@ private:
             // TODO: rework to have a single draft llama_context shared across all slots [TAG_SERVER_SPEC_REWORK]
             //       perform the speculative drafting for all sequences at the same time in a single batch
             const int n_draft_max = (slot.spec_n_accepted > 0) ? slot.spec_n_accepted : slot.get_n_draft_max();
-            if (n_draft_max > 0 && slot.spec_n_denials < params_base.speculative.ckpt_num_tries) {
+            if (n_draft_max > 0 && (params_base.speculative.ckpt_num_tries == 0
+                                    || slot.spec_n_denials < params_base.speculative.ckpt_num_tries)) {
                 if (mctx) {
                     // we should never reach this, as speculative is automatically disabled if mmproj is loaded
                     GGML_ABORT("not supported by multimodal");
@@ -2068,16 +2074,19 @@ private:
 
                 const auto pos_min = llama_memory_seq_pos_min(llama_get_memory(ctx), slot.id);
                 const auto pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx), slot.id);
-                bool do_checkpoint = !draft.empty() && slot.prompt.checkpoints.size() < (size_t) params_base.n_ctx_checkpoints;
-                SLT_DBG(slot, "draft.size = %zu, n_spec_denials = %d, #ckpts=%zu, do_checkpoint = %s, pos_min = %d, pos_max = %d, tokens=[..., %d, %d, %d]\n",
-                    draft.size(), slot.spec_n_denials,
-                    slot.prompt.checkpoints.size(),
-                    do_checkpoint ? "yes" : "no", pos_min, pos_max,
-                    cached_text_tokens[cached_text_tokens.size() - 3],
-                    cached_text_tokens[cached_text_tokens.size() - 2],
-                    cached_text_tokens[cached_text_tokens.size() - 1]);
+                bool do_checkpoint = !draft.empty() && params_base.speculative.ckpt_num_tries > 0
+                    && slot.prompt.checkpoints.size() < (size_t) params_base.n_ctx_checkpoints;
+                if (do_checkpoint && cached_text_tokens.size() > 5) {
+                    SLT_DBG(slot, "draft.size = %zu, n_spec_denials = %d, #ckpts=%zu, do_checkpoint = %s, pos_min = %d, pos_max = %d, tokens=[..., %d, %d, %d]\n",
+                        draft.size(), slot.spec_n_denials,
+                        slot.prompt.checkpoints.size(),
+                        do_checkpoint ? "yes" : "no", pos_min, pos_max,
+                        cached_text_tokens[cached_text_tokens.size() - 3],
+                        cached_text_tokens[cached_text_tokens.size() - 2],
+                        cached_text_tokens[cached_text_tokens.size() - 1]);
+                }
 
-               if (do_checkpoint) {
+                if (do_checkpoint) {
                     while (slot.prompt.checkpoints.size() >= (size_t) params_base.n_ctx_checkpoints) {
                         // make room for the new checkpoint, if needed
                         const auto & cur = slot.prompt.checkpoints.front();
@@ -2859,7 +2868,7 @@ private:
                 const int64_t t_current = ggml_time_us();
 
 
-                if (ids.size() < n_draft + 1 && slot.spec_has_ckpt) {
+                if (slot.spec_has_ckpt && ids.size() < n_draft + 1) {
                     // the main model rejected some tokens, so we need to rollback to the state before sampling the draft tokens
                     auto & ckpt = slot.prompt.checkpoints.back();
                     SLT_INF(slot, "partial acceptance: %zu < %zu, restoring checkpoint (pos_min = %d, pos_max = %d)\n",