Google's Neural Machine Translation (NMT) system, GNMT, addresses the computational costs and robustness issues of traditional NMT systems. GNMT uses a deep LSTM network with 8 encoder and 8 decoder layers, incorporating residual connections and attention mechanisms to improve parallelism and speed. To handle rare words, GNMT employs sub-word units ("wordpieces") that balance flexibility and efficiency. The system also includes a beam search technique with length normalization and a coverage penalty to ensure comprehensive translation. GNMT achieves competitive results on WMT'14 English-to-French and English-to-German benchmarks, reducing translation errors by 60% compared to Google's phrase-based system. The model is robust across various language pairs and outperforms previous state-of-the-art systems.Google's Neural Machine Translation (NMT) system, GNMT, addresses the computational costs and robustness issues of traditional NMT systems. GNMT uses a deep LSTM network with 8 encoder and 8 decoder layers, incorporating residual connections and attention mechanisms to improve parallelism and speed. To handle rare words, GNMT employs sub-word units ("wordpieces") that balance flexibility and efficiency. The system also includes a beam search technique with length normalization and a coverage penalty to ensure comprehensive translation. GNMT achieves competitive results on WMT'14 English-to-French and English-to-German benchmarks, reducing translation errors by 60% compared to Google's phrase-based system. The model is robust across various language pairs and outperforms previous state-of-the-art systems.