diff --git a/examples/applications/computing-embeddings/computing_embeddings_multi_cpu.py b/examples/applications/computing-embeddings/computing_embeddings_multi_cpu.py
new file mode 100644
index 000000000..4ec90635a
--- /dev/null
+++ b/examples/applications/computing-embeddings/computing_embeddings_multi_cpu.py
@@ -0,0 +1,39 @@
+"""
+This example demonstrates how to use parallel computing with the tqdm package
+to show progress while encoding sentences using the SentenceTransformer model.
+"""
+
+import logging
+from multiprocessing import Pool, cpu_count
+
+from tqdm import tqdm
+
+from sentence_transformers import LoggingHandler, SentenceTransformer
+
+logging.basicConfig(
+    format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO, handlers=[LoggingHandler()]
+)
+
+
+def encode_sentences(sentences):
+    model = SentenceTransformer("all-MiniLM-L6-v2")
+    return model.encode(sentences)
+
+
+if __name__ == "__main__":
+    # Create a large list of sentences
+    sentences = [f"This is sentence {i}" for i in range(100_000)]
+
+    # Split sentences into chunks for parallel processing
+    num_chunks = cpu_count()
+    chunk_size = len(sentences) // num_chunks
+    sentence_chunks = [sentences[i * chunk_size:(i + 1) * chunk_size] for i in range(num_chunks)]
+
+    # Use multiprocessing Pool to encode sentences in parallel
+    with Pool(processes=num_chunks) as pool:
+        results = list(tqdm(pool.imap(encode_sentences, sentence_chunks), total=num_chunks))
+
+    # Flatten the list of results
+    sentence_embeddings = [embedding for result in results for embedding in result]
+
+    print("Embeddings computed. Number of embeddings:", len(sentence_embeddings))