diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index 887c75561..e0db33a37 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -144,7 +144,7 @@ int main(int argc, char ** argv) {
             fprintf(stderr, "%s: testing memory usage for n_batch = %d, n_ctx = %d\n", __func__, params.n_batch, params.n_ctx);
 
             const std::vector<llama_token> tmp(params.n_batch, llama_token_bos());
-            llama_eval(ctx, tmp.data(), tmp.size(), params.n_ctx, params.n_threads, params.pp_threads);
+            llama_eval(ctx, tmp.data(), tmp.size(), params.n_ctx, params.n_threads);
         }
 
         llama_print_timings(ctx);
@@ -406,7 +406,7 @@ int main(int argc, char ** argv) {
     // do one empty run to warm up the model
     {
         const std::vector<llama_token> tmp = { llama_token_bos(), };
-        llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads, params.pp_threads);
+        llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
         llama_reset_timings(ctx);
     }
 
@@ -513,7 +513,8 @@ int main(int argc, char ** argv) {
 
                 for (int i = 0; i < input_size; i += params.n_batch) {
                     int n_eval = std::min(input_size - i, params.n_batch);
-                    if (llama_eval(ctx_guidance, input_buf + i, n_eval, n_past_guidance, params.n_threads, params.pp_threads)) {
+                    int eval_thr = n_eval > 1 ? params.pp_threads : params.n_threads;
+                    if (llama_eval(ctx_guidance, input_buf + i, n_eval, n_past_guidance, eval_thr)) {
                         fprintf(stderr, "%s : failed to eval\n", __func__);
                         return 1;
                     }
@@ -527,7 +528,8 @@ int main(int argc, char ** argv) {
                 if (n_eval > params.n_batch) {
                     n_eval = params.n_batch;
                 }
-                if (llama_eval(ctx, &embd[i], n_eval, n_past, params.n_threads, params.pp_threads)) {
+                int eval_thr = n_eval > 1 ? params.pp_threads : params.n_threads;
+                if (llama_eval(ctx, &embd[i], n_eval, n_past, eval_thr)) {
                     fprintf(stderr, "%s : failed to eval\n", __func__);
                     return 1;
                 }
diff --git a/llama.cpp b/llama.cpp
index 7c009ddee..ef2672ae9 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -1787,7 +1787,6 @@ static struct ggml_cgraph * llama_build_graph(
 //   - n_tokens   number of tokens
 //   - n_past:    the context size so far
 //   - n_threads: number of threads to use for inference
-//   - pp_threads: number of threads to use for prompt processing
 //
 static bool llama_eval_internal(
          llama_context & lctx,
@@ -1796,7 +1795,6 @@ static bool llama_eval_internal(
                    int   n_tokens,
                    int   n_past,
                    int   n_threads,
-                   int   pp_threads,
             const char * cgraph_fname) {
 
     LLAMA_ASSERT((!tokens && embd) || (tokens && !embd));
@@ -1840,8 +1838,7 @@ static bool llama_eval_internal(
 
     // for big prompts, if BLAS is enabled, it is better to use only one thread
     // otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
-    pp_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : pp_threads;
-    n_threads = N > 1 ? pp_threads : n_threads;
+    n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
 
     struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1];
     struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 2];
@@ -3487,7 +3484,7 @@ struct llama_context * llama_new_context_with_model(
     if (ggml_mpi_rank(ctx->ctx_mpi) > 0) {
         // Enter a blocking eval loop with dummy input, letting rank=0 drive the process
         const std::vector<llama_token> tmp(ctx->model.hparams.n_ctx, llama_token_bos());
-        while (!llama_eval(ctx, tmp.data(), tmp.size(), 0, 0, 0)) {};
+        while (!llama_eval(ctx, tmp.data(), tmp.size(), 0, 0)) {};
         llama_backend_free();
         exit(1);
     }
@@ -4179,9 +4176,8 @@ int llama_eval(
            const llama_token * tokens,
                          int   n_tokens,
                          int   n_past,
-                         int   n_threads,
-                         int   pp_threads) {
-    if (!llama_eval_internal(*ctx, tokens, nullptr, n_tokens, n_past, n_threads, pp_threads, nullptr)) {
+                         int   n_threads) {
+    if (!llama_eval_internal(*ctx, tokens, nullptr, n_tokens, n_past, n_threads, nullptr)) {
         LLAMA_LOG_ERROR("%s: failed to eval\n", __func__);
         return 1;
     }
@@ -4202,9 +4198,8 @@ int llama_eval_embd(
                      const float * embd,
                              int   n_tokens,
                              int   n_past,
-                             int   n_threads,
-                             int   pp_threads) {
-    if (!llama_eval_internal(*ctx, nullptr, embd, n_tokens, n_past, n_threads, pp_threads, nullptr)) {
+                             int   n_threads) {
+    if (!llama_eval_internal(*ctx, nullptr, embd, n_tokens, n_past, n_threads, nullptr)) {
         LLAMA_LOG_ERROR("%s: failed to eval\n", __func__);
         return 1;
     }
@@ -4225,7 +4220,7 @@ int llama_eval_export(struct llama_context * ctx, const char * fname) {
 
     const std::vector<llama_token> tmp(n_batch, llama_token_bos());
 
-    if (!llama_eval_internal(*ctx, tmp.data(), nullptr, tmp.size(), n_ctx, 1, 1, fname)) {
+    if (!llama_eval_internal(*ctx, tmp.data(), nullptr, tmp.size(), n_ctx, 1, fname)) {
         LLAMA_LOG_ERROR("%s: failed to eval\n", __func__);
         return 1;
     }
diff --git a/llama.h b/llama.h
index addeae45c..9d732f914 100644
--- a/llama.h
+++ b/llama.h
@@ -308,8 +308,7 @@ extern "C" {
                const llama_token * tokens,
                              int   n_tokens,
                              int   n_past,
-                             int   n_threads,
-                             int   pp_threads);
+                             int   n_threads);
 
     // Same as llama_eval, but use float matrix input directly.
     LLAMA_API int llama_eval_embd(
@@ -317,8 +316,7 @@ extern "C" {
                      const float * embd,
                              int   n_tokens,
                              int   n_past,
-                             int   n_threads,
-                             int   pp_threads);
+                             int   n_threads);
 
     // Export a static computation graph for context of 511 and batch size of 1
     // NOTE: since this functionality is mostly for debugging and demonstration purposes, we hardcode these