What is the batch size used during training DeepSeek's R1 model?

The batch size used during training for DeepSeek’s R1 model has not been explicitly disclosed in public documentation. However, based on common practices in training large language models (LLMs) and technical considerations, we can infer the general approach and trade-offs involved. Batch size refers to the number of training examples processed in one forward/backward pass during training. For models like R1, which are designed for high performance and scalability, batch sizes typically range from hundreds to thousands of examples per batch, depending on hardware constraints and optimization goals. For example, similar models like GPT-3 used batch sizes of 3.2 million tokens (not examples) per iteration, which translates to roughly 1,000-2,000 sequences per batch when accounting for sequence length.

The choice of batch size involves balancing computational efficiency and model performance. Larger batches enable faster training by leveraging parallel processing on GPUs or TPUs but require more memory and may reduce convergence speed. Smaller batches use less memory and can improve generalization but increase training time. For DeepSeek R1, the batch size was likely determined by available hardware (e.g., cluster size, GPU/TPU memory) and techniques like gradient accumulation, which allows simulating larger batches by aggregating gradients across multiple smaller batches. Additionally, frameworks like Megatron-LM or DeepSpeed are often used to optimize distributed training, enabling efficient scaling of batch sizes across hundreds or thousands of devices.

Developers training similar models can experiment with batch sizes based on their infrastructure. For instance, a batch size of 1,024 samples per GPU is common in setups using NVIDIA A100 GPUs with 40GB memory, while larger clusters might use batch sizes in the tens of thousands. DeepSeek R1’s architecture likely employed adaptive strategies, such as dynamic batching or mixed precision training, to maximize throughput without exceeding hardware limits. While the exact batch size remains undisclosed, understanding these principles helps developers make informed choices when replicating or adapting such models for their own projects.