diff --git a/examples/test_optimized_no_streaming.py b/examples/test_optimized_no_streaming.py
new file mode 100644
index 0000000..c186f74
--- /dev/null
+++ b/examples/test_optimized_no_streaming.py
@@ -0,0 +1,202 @@
+"""
+Test TTS with optimizations (non-streaming mode).
+
+Uses the same optimizations as streaming for ~4x speedup:
+1. generate_fast() - bypasses HuggingFace generate() overhead (2-3x speedup)
+2. torch.compile for decoder with max-autotune mode
+3. Compiled codebook predictor
+
+Usage:
+    cd Qwen3-TTS
+    python examples/test_optimized_no_streaming.py
+"""
+
+import time
+import numpy as np
+import torch
+import soundfile as sf
+from qwen_tts import Qwen3TTSModel
+
+# Enable TensorFloat32 for better performance on Ampere+ GPUs
+torch.set_float32_matmul_precision('high')
+
+
+def log_time(start, operation):
+    elapsed = time.time() - start
+    print(f"[{elapsed:.2f}s] {operation}")
+    return time.time()
+
+
+def run_generation(
+    model,
+    text: str,
+    language: str,
+    voice_clone_prompt,
+    label: str = "generation",
+):
+    """Run non-streaming generation and return timing stats."""
+    start = time.time()
+
+    wavs, sr = model.generate_voice_clone(
+        text=text,
+        language=language,
+        voice_clone_prompt=voice_clone_prompt,
+    )
+
+    total_time = time.time() - start
+    audio = wavs[0] if wavs else np.array([])
+
+    audio_duration = len(audio) / sr if sr > 0 else 0
+
+    return {
+        "label": label,
+        "total_time": total_time,
+        "audio": audio,
+        "sample_rate": sr,
+        "audio_duration": audio_duration,
+    }
+
+
+def main():
+    total_start = time.time()
+
+    print("=" * 60)
+    print("Loading model...")
+    print("=" * 60)
+
+    start = time.time()
+    model = Qwen3TTSModel.from_pretrained(
+        "Qwen/Qwen3-TTS-12Hz-1.7B-Base",
+        device_map="cuda:0",
+        dtype=torch.bfloat16,
+        attn_implementation="flash_attention_2",
+    )
+    log_time(start, "Model loaded")
+
+    # Reference audio setup
+    ref_audio_path = "../neurona-10sec.wav"
+    ref_text = (
+        "Обоссышься точно, я короче твои цветы продала, цветы с подоконника, рюкзак, сменку, "
+        "пару парт, ща еще окна и сторожа еще смотри приедут. А У тебя кстати родители. "
+        "Перед тобой Тони Старк, только после пту. Стив Джобс, только с контузией. Илон Макс, не Маск. "
+    )
+
+    start = time.time()
+    voice_clone_prompt = model.create_voice_clone_prompt(
+        ref_audio=ref_audio_path,
+        ref_text=ref_text,
+    )
+    log_time(start, "Voice clone prompt created")
+
+    # Test text
+    test_text = "Привет всем! Я того всё ебала, что за новый голос тут на обзоре у вилсакома? А? Так он мне понравился. Ганс оф буллщит."
+
+    results = []
+
+    # ============== Test 1: Standard generation (baseline) ==============
+    print("\n" + "=" * 60)
+    print("Test 1: Standard generation (baseline)")
+    print("=" * 60)
+
+    result = run_generation(
+        model, test_text, "Russian", voice_clone_prompt,
+        label="baseline",
+    )
+    results.append(result)
+    sf.write("output_baseline.wav", result["audio"], result["sample_rate"])
+    rtf = result['total_time'] / result['audio_duration'] if result['audio_duration'] > 0 else 0
+    print(f"Total: {result['total_time']:.2f}s, Audio: {result['audio_duration']:.2f}s, RTF: {rtf:.2f}")
+
+    # ============== Test 2: With optimizations ==============
+    print("\n" + "=" * 60)
+    print("Test 2: With optimizations (fast codebook + compiled decoder)")
+    print("=" * 60)
+
+    # Enable optimizations - using the same method as streaming but tuned for batch
+    print("\nEnabling optimizations...")
+    model.enable_streaming_optimizations(
+        decode_window_frames=300,  # Larger window for non-streaming
+        use_compile=True,
+        use_cuda_graphs=False,  # Not needed for non-streaming (variable sizes)
+        compile_mode="max-autotune",  # Better for batch processing than reduce-overhead
+        use_fast_codebook=True,  # KEY: 2-3x speedup by bypassing HF generate()
+        compile_codebook_predictor=True,  # Compile the codebook predictor too
+    )
+
+    # Warmup run (first run after compile is slower due to compilation)
+    print("\nWarmup run (first run after compile)...")
+    warmup_result = run_generation(
+        model, "Тест один два три четыре пять.", "Russian", voice_clone_prompt,
+        label="warmup",
+    )
+    warmup_rtf = warmup_result['total_time'] / warmup_result['audio_duration'] if warmup_result['audio_duration'] > 0 else 0
+    print(f"Warmup: Total: {warmup_result['total_time']:.2f}s, Audio: {warmup_result['audio_duration']:.2f}s, RTF: {warmup_rtf:.2f}")
+
+    # Actual test run
+    print("\nOptimized test run...")
+    result = run_generation(
+        model, test_text, "Russian", voice_clone_prompt,
+        label="optimized",
+    )
+    results.append(result)
+    sf.write("output_optimized.wav", result["audio"], result["sample_rate"])
+    opt_rtf = result['total_time'] / result['audio_duration'] if result['audio_duration'] > 0 else 0
+    print(f"Total: {result['total_time']:.2f}s, Audio: {result['audio_duration']:.2f}s, RTF: {opt_rtf:.2f}")
+
+    # Second optimized run to show stable performance
+    print("\nSecond optimized run...")
+    result2 = run_generation(
+        model, test_text, "Russian", voice_clone_prompt,
+        label="optimized_2",
+    )
+    results.append(result2)
+    opt2_rtf = result2['total_time'] / result2['audio_duration'] if result2['audio_duration'] > 0 else 0
+    print(f"Total: {result2['total_time']:.2f}s, Audio: {result2['audio_duration']:.2f}s, RTF: {opt2_rtf:.2f}")
+
+    # Third run for stability check
+    print("\nThird optimized run...")
+    result3 = run_generation(
+        model, test_text, "Russian", voice_clone_prompt,
+        label="optimized_3",
+    )
+    results.append(result3)
+    opt3_rtf = result3['total_time'] / result3['audio_duration'] if result3['audio_duration'] > 0 else 0
+    print(f"Total: {result3['total_time']:.2f}s, Audio: {result3['audio_duration']:.2f}s, RTF: {opt3_rtf:.2f}")
+
+    # ============== Summary ==============
+    print("\n" + "=" * 80)
+    print("SUMMARY")
+    print("=" * 80)
+
+    baseline_total = results[0]["total_time"]
+
+    print(f"\n{'Method':<20} {'Total':>10} {'Audio':>10} {'RTF':>8} {'Speedup':>10}")
+    print("-" * 60)
+
+    for r in results:
+        total = r["total_time"]
+        audio_dur = r.get("audio_duration", 0)
+        rtf = total / audio_dur if audio_dur > 0 else 0
+        speedup = baseline_total / total if total > 0 else 0
+        print(f"{r['label']:<20} {total:>9.2f}s {audio_dur:>9.2f}s {rtf:>8.2f} {speedup:>9.2f}x")
+
+    print(f"\n[{time.time() - total_start:.2f}s] TOTAL SCRIPT TIME")
+
+    # Tips
+    print("\n" + "=" * 60)
+    print("OPTIMIZATIONS APPLIED")
+    print("=" * 60)
+    print("""
+1. torch.set_float32_matmul_precision('high') - TensorFloat32 on Ampere+ GPUs
+2. bfloat16 dtype - faster computation with minimal quality loss
+3. flash_attention_2 - efficient attention computation
+4. use_fast_codebook=True - bypasses HuggingFace generate() for 2-3x speedup
+5. torch.compile with max-autotune mode for decoder
+6. compile_codebook_predictor=True - compiled code predictor
+
+Expected speedup: 2.5-4x over baseline
+""")
+
+
+if __name__ == "__main__":
+    main()