diff --git a/main.cpp b/main.cpp
index fbb43a8cc..da42645f3 100644
--- a/main.cpp
+++ b/main.cpp
@@ -90,17 +90,26 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
     int count = 0;
     double nll = 0.0;
     int seq_count = tokens.size() / params.n_ctx;
+    int n_vocab = llama_n_vocab(ctx);
 
-    fprintf(stderr, "%s : calculating perplexity over %d chunks\n", __func__, seq_count);
+    fprintf(stderr, "%s : calculating perplexity over %d chunks, batch_size=%d\n", __func__, seq_count, params.n_batch);
 
     for (int i = 0; i < seq_count; ++i) {
         int start = i * params.n_ctx;
         int end = start + params.n_ctx - 1;
-        std::vector<llama_token> embd(tokens.begin() + start, tokens.begin() + end);
+
+        std::vector<float> logits;
+        int num_batches = (params.n_ctx + params.n_batch - 1) / params.n_batch;
         auto start_t = std::chrono::high_resolution_clock::now();
-        if (llama_eval(ctx, embd.data(), embd.size(), 0, params.n_threads)) {
-            fprintf(stderr, "%s : failed to eval\n", __func__);
-            return;
+        for (int j = 0; j < num_batches; ++j) {
+            int batch_start = start + j * params.n_batch;
+            int batch_size = std::min(end - batch_start, params.n_batch);
+            if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * params.n_batch, params.n_threads)) {
+                fprintf(stderr, "%s : failed to eval\n", __func__);
+                return;
+            }
+            auto batch_logits = llama_get_logits(ctx);
+            logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
         }
         auto end_t = std::chrono::high_resolution_clock::now();
         if (i == 0) {
@@ -120,13 +129,11 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
         // last 256 tokens.  Then, we split the input up into context window size chunks to
         // process the entire prompt.
 
-        auto logits = llama_get_logits(ctx);
         for (int j = params.n_ctx / 2; j < params.n_ctx - 1; ++j) {
             // Calculate probability of next token, given the previous ones.
-            int n_vocab = llama_n_vocab(ctx);
             std::vector<float> tok_logits(
-                logits + j * n_vocab,
-                logits + (j + 1) * n_vocab);
+                logits.begin() + j * n_vocab,
+                logits.begin() + (j + 1) * n_vocab);
             double prob = softmax(tok_logits)[tokens[start + j + 1]];
             nll += -std::log(prob);
             ++count;