What hardware infrastructure does DeepSeek use for training its models?

DeepSeek uses a combination of high-performance computing hardware optimized for large-scale machine learning training. Their infrastructure relies on clusters of GPUs, specifically NVIDIA A100 and H100 Tensor Core GPUs, which provide the parallel processing power needed for training models with billions of parameters. These GPUs are interconnected using high-speed networking technologies like NVIDIA InfiniBand, which reduces communication bottlenecks during distributed training. The clusters are designed to scale horizontally, allowing DeepSeek to allocate hundreds or thousands of GPUs to a single training job, depending on the model size and complexity. For example, training a model like DeepSeek-R1, which has over 100 billion parameters, likely requires a multi-node setup with dedicated memory and compute resources to handle the massive matrix operations efficiently.

The training environment is supported by a software stack that manages distributed computing and resource allocation. Frameworks like PyTorch with Fully Sharded Data Parallel (FSDP) or Microsoft’s DeepSpeed are used to split models and data across GPUs, enabling efficient memory usage and reducing training time. DeepSeek also employs optimized data pipelines to preprocess and feed training data at scale. For instance, datasets are stored in distributed file systems like Lustre or cloud-based object storage, with data loading pipelines using tools like Apache Arrow or WebDataset to minimize I/O latency. This setup ensures that GPUs remain fully utilized during training, avoiding idle time caused by data transfer delays. Additionally, checkpointing systems and fault-tolerant workflows help recover from hardware failures without losing progress, which is critical for long-running training jobs spanning weeks.

To maximize efficiency, DeepSeek integrates hardware-specific optimizations. For example, they leverage mixed-precision training (FP16/FP8) on NVIDIA GPUs to accelerate computations while managing memory constraints. Custom kernels, written in CUDA or using compiler frameworks like Triton, are used to optimize critical operations such as attention mechanisms in transformer models. The infrastructure also includes monitoring tools like Prometheus and Grafana to track GPU utilization, power consumption, and network throughput in real time. Energy efficiency is prioritized through liquid cooling systems and power-aware scheduling, reducing operational costs. These optimizations allow DeepSeek to balance computational performance with practical constraints, ensuring that resources are used effectively without compromising training stability or model quality.