diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index a5345ee2a4..fb35d6c79d 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -1155,16 +1155,25 @@ int llama_context::decode(const llama_batch & batch_inp) {
 
         // extract logits
         if (t_logits && n_outputs > 0) {
-            ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(sched.get(), t_logits);
-            GGML_ASSERT(backend_res != nullptr);
-            GGML_ASSERT(logits != nullptr);
+            // MTP operations that are purely for updating the KV cache
+            // (MTP_OP_WARMUP and MTP_OP_UPDATE_ACCEPTED) also produce a logit tensor
+            // as a side effect of running the graph. If these logits are copied
+            // back to the main context buffer, they will overwrite the valid logits
+            // produced by the main model's pass, leading to incorrect sampling.
+            // This condition explicitly prevents that copy for cache-only operations.
+            if (batch_inp.mtp_params.op_type != MTP_OP_WARMUP &&
+                batch_inp.mtp_params.op_type != MTP_OP_UPDATE_ACCEPTED) {
+                ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(sched.get(), t_logits);
+                GGML_ASSERT(backend_res != nullptr);
+                GGML_ASSERT(logits != nullptr);
 
-            float * logits_out = logits + n_outputs_prev*n_vocab;
+                float * logits_out = logits + n_outputs_prev*n_vocab;
 
-            if (n_outputs) {
-                GGML_ASSERT( n_outputs_prev + n_outputs <= n_outputs_all);
-                GGML_ASSERT((n_outputs_prev + n_outputs)*n_vocab <= (int64_t) logits_size);
-                ggml_backend_tensor_get_async(backend_res, t_logits, logits_out, 0, n_outputs*n_vocab*sizeof(float));
+                if (n_outputs) {
+                    GGML_ASSERT( n_outputs_prev + n_outputs <= n_outputs_all);
+                    GGML_ASSERT((n_outputs_prev + n_outputs)*n_vocab <= (int64_t) logits_size);
+                    ggml_backend_tensor_get_async(backend_res, t_logits, logits_out, 0, n_outputs*n_vocab*sizeof(float));
+                }
             }
         }