DeepSeek primarily uses high-performance computing hardware for training its models, specifically leveraging Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs). These types of hardware are designed to handle the large-scale matrix calculations and parallel processing required for deep learning tasks. For instance, NVIDIA’s A100 or V100 GPUs are commonly utilized due to their capacity for handling heavy workloads, efficient memory management, and support for deep learning frameworks like TensorFlow and PyTorch. TPUs, developed by Google, can also be employed for certain tasks, providing enhanced performance for specific types of machine learning models.
In addition to GPUs and TPUs, DeepSeek may also deploy multiple servers in cluster configurations, enabling distributed training. This approach allows the system to train models faster by breaking down the tasks into smaller chunks and processing them simultaneously across different machines. For example, using a cluster of high-performance servers equipped with dozens of GPUs could significantly decrease the time needed for model training, making it feasible to experiment with larger datasets or more complex models.
Finally, storage systems and networking infrastructure are crucial to DeepSeek's hardware setup. Fast Solid-State Drives (SSDs) are typically used to store the datasets and models, providing quick read/write speeds that are essential for large volumes of data. Moreover, high-speed networking is important for ensuring that data can move efficiently between devices in the training process. Overall, the integration of powerful GPUs or TPUs, clustered setups, and robust storage solutions creates an effective environment for training DeepSeek's models.