DeepSeek trains its models using a combination of high-performance GPUs, distributed computing infrastructure, and optimized software frameworks. The primary hardware consists of NVIDIA GPUs, which are widely used in the industry for their parallel processing capabilities and compatibility with machine learning libraries. To handle large-scale training tasks, DeepSeek employs clusters of these GPUs connected via high-speed networking, enabling efficient communication between nodes during distributed training. This setup allows them to scale training across thousands of GPUs, reducing the time required to train complex models.
The specific GPU models used likely include NVIDIA A100 and H100 Tensor Core GPUs, which are designed for AI workloads. These GPUs provide significant memory bandwidth (e.g., 1.5–2 TB/s on H100) and support for mixed-precision training, which accelerates computation while maintaining model accuracy. For inter-GPU communication, DeepSeek probably relies on technologies like NVLink (for direct GPU-to-GPU connections within a server) and InfiniBand (for high-throughput, low-latency networking between servers). These technologies minimize bottlenecks when synchronizing model parameters across nodes. Additionally, custom in-house optimizations, such as kernel fusion or memory management tweaks, might be applied to maximize hardware utilization.
On the software side, DeepSeek likely uses frameworks like PyTorch or TensorFlow, combined with distributed training libraries such as DeepSpeed or Horovod, to manage parallelism across GPUs. They might also leverage NVIDIA’s CUDA and cuDNN libraries for low-level GPU acceleration. To handle data storage and preprocessing, a distributed file system (e.g., Lustre) or object storage solutions could be in place, paired with data pipelines optimized for throughput. Monitoring tools like Prometheus or Grafana might track cluster health, while orchestration systems like Kubernetes or SLURM manage job scheduling. This combination of hardware and software allows DeepSeek to efficiently train models at scale while maintaining flexibility to experiment with architectures and training techniques.