diff --git a/src/llama.cpp b/src/llama.cpp
index 465938cf0..e89e70bbe 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -7629,30 +7629,6 @@ static int llama_decode_impl(
             return -3;
         }
 
-        // reserve a worst case graph if needed
-        // TODO: extract to a function
-        if (lctx.need_reserve) {
-            const auto & cparams = lctx.cparams;
-            const auto & model   = lctx.model;
-
-            // build worst-case graph
-            uint32_t n_seqs = 1; // TODO: worst-case number of sequences
-            uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
-
-            llama_token token = model.vocab.token_bos(); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
-            llama_ubatch ubatch = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
-
-            ggml_cgraph * gf = llama_build_graph(lctx, ubatch, true);
-
-            // initialize scheduler with the worst-case graph
-            ggml_backend_sched_reset(lctx.sched.get());
-            if (!ggml_backend_sched_reserve(lctx.sched.get(), gf)) {
-                LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
-            }
-
-            lctx.need_reserve = false;
-        }
-
         ggml_backend_sched_reset(lctx.sched.get());
         ggml_backend_sched_set_eval_callback(lctx.sched.get(), lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data);
 
@@ -7889,30 +7865,8 @@ static int llama_encode_impl(
 
     //batch_manager->prepare(ubatch);
 
-    // reserve a worst case graph if needed
-    // TODO: extract to a function
-    if (lctx.need_reserve) {
-        // TODO: extract to a function
-        const auto & cparams = lctx.cparams;
-        const auto & model   = lctx.model;
-
-        // build worst-case graph
-        uint32_t n_seqs = 1; // TODO: worst-case number of sequences
-        uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
-
-        llama_token token = model.vocab.token_bos(); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
-        llama_ubatch ubatch = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
-
-        ggml_cgraph * gf = llama_build_graph(lctx, ubatch, true);
-
-        // initialize scheduler with the worst-case graph
-        ggml_backend_sched_reset(lctx.sched.get());
-        if (!ggml_backend_sched_reserve(lctx.sched.get(), gf)) {
-            LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
-        }
-
-        lctx.need_reserve = false;
-    }
+    // TODO: do reserve
+    GGML_ASSERT(lctx.need_reserve == false);
 
     ggml_backend_sched_reset(lctx.sched.get());
     ggml_backend_sched_set_eval_callback(lctx.sched.get(), lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data);