How does DeepSeek manage distributed training across multiple GPUs?

DeepSeek manages distributed training across multiple GPUs by combining data parallelism, model parallelism, and efficient communication strategies. The system splits large models and datasets across GPUs to balance computational load and memory usage. For example, in data parallelism, each GPU holds a copy of the entire model and processes a subset of the training data. Gradients from each GPU are averaged during synchronization to update the model consistently. For very large models that don’t fit on a single GPU, DeepSeek uses model parallelism, partitioning layers or tensors across devices. Techniques like pipeline parallelism or tensor slicing ensure minimal communication overhead while maintaining training efficiency.

Communication between GPUs is optimized using frameworks like NCCL (NVIDIA Collective Communications Library) for high-speed data transfer and synchronization. DeepSeek employs gradient accumulation and all-reduce operations to handle distributed updates efficiently. For instance, when using mixed-precision training, gradients are computed in FP16 but aggregated in FP32 to maintain numerical stability. The system also minimizes idle time by overlapping computation and communication—such as preprocessing the next batch while transferring gradients. Tools like PyTorch’s DistributedDataParallel (DDP) or DeepSpeed’s ZeRO (Zero Redundancy Optimizer) are often integrated to automate sharding and reduce memory redundancy.

To handle scalability and fault tolerance, DeepSeek implements checkpointing and dynamic resource allocation. Checkpoints are saved periodically to resume training in case of hardware failures. Memory optimizations, such as activation checkpointing (recomputing intermediate values during backward passes), reduce GPU memory consumption. For multi-node setups, the system coordinates communication via message-passing interfaces (MPI) or Ethernet/RDMA networks. Developers can configure batch sizes, parallelism strategies, and communication intervals through APIs, balancing speed and hardware constraints. Monitoring tools like TensorBoard or custom dashboards track metrics like GPU utilization and gradient norms, enabling fine-tuning of distributed workflows.