This paper introduces a new approach to language modeling using gated convolutional networks (GCNNs), which outperforms traditional recurrent neural networks (RNNs) and long short-term memory (LSTM) models. The proposed method uses stacked convolutions to capture long-term dependencies in text, allowing for efficient parallel processing over sequential tokens. A novel simplified gating mechanism is introduced, which outperforms previous methods and enables the model to achieve state-of-the-art results on the WikiText103 benchmark, even though it features long-term dependencies. The model also performs competitively on the Google Billion Words benchmark. The GCNN reduces the latency to score a sentence by an order of magnitude compared to a recurrent baseline, making it a more efficient alternative for large-scale language tasks. The approach involves using gated linear units (GLUs) to control the flow of information through the network, which helps mitigate the vanishing gradient problem. The model is trained on two large-scale language modeling datasets: the Google Billion Word dataset and WikiText-103. The GCNN outperforms other models, including LSTMs, on both datasets, achieving lower perplexity and faster convergence. The model's hierarchical structure allows it to capture long-range dependencies more effectively than RNNs, which are inherently sequential and require linear operations over the input sequence. The GCNN is also more computationally efficient than RNNs, as it can be parallelized over both sequences and individual tokens within a sequence. This allows for faster training and inference, with the GCNN achieving significantly higher throughput and responsiveness compared to RNNs. The model's performance is further improved by using adaptive softmax, which reduces memory requirements and speeds up computation. The results show that the GCNN can achieve strong performance with a much smaller model size and fewer resources compared to RNNs. The paper also evaluates the impact of different gating mechanisms and finds that the GLU provides a linear path for gradients, allowing for faster convergence and better performance. The GCNN is shown to be effective in capturing long-range dependencies, even with a fixed context size, and performs well on both small and large datasets. The results demonstrate that the GCNN is a competitive alternative to RNNs for large-scale language modeling tasks.This paper introduces a new approach to language modeling using gated convolutional networks (GCNNs), which outperforms traditional recurrent neural networks (RNNs) and long short-term memory (LSTM) models. The proposed method uses stacked convolutions to capture long-term dependencies in text, allowing for efficient parallel processing over sequential tokens. A novel simplified gating mechanism is introduced, which outperforms previous methods and enables the model to achieve state-of-the-art results on the WikiText103 benchmark, even though it features long-term dependencies. The model also performs competitively on the Google Billion Words benchmark. The GCNN reduces the latency to score a sentence by an order of magnitude compared to a recurrent baseline, making it a more efficient alternative for large-scale language tasks. The approach involves using gated linear units (GLUs) to control the flow of information through the network, which helps mitigate the vanishing gradient problem. The model is trained on two large-scale language modeling datasets: the Google Billion Word dataset and WikiText-103. The GCNN outperforms other models, including LSTMs, on both datasets, achieving lower perplexity and faster convergence. The model's hierarchical structure allows it to capture long-range dependencies more effectively than RNNs, which are inherently sequential and require linear operations over the input sequence. The GCNN is also more computationally efficient than RNNs, as it can be parallelized over both sequences and individual tokens within a sequence. This allows for faster training and inference, with the GCNN achieving significantly higher throughput and responsiveness compared to RNNs. The model's performance is further improved by using adaptive softmax, which reduces memory requirements and speeds up computation. The results show that the GCNN can achieve strong performance with a much smaller model size and fewer resources compared to RNNs. The paper also evaluates the impact of different gating mechanisms and finds that the GLU provides a linear path for gradients, allowing for faster convergence and better performance. The GCNN is shown to be effective in capturing long-range dependencies, even with a fixed context size, and performs well on both small and large datasets. The results demonstrate that the GCNN is a competitive alternative to RNNs for large-scale language modeling tasks.