DeepSeek's approach to AI training reflects a broader trend in the industry where companies are increasingly focused on optimizing both training and inference processes to enhance model performance.

DeepSeek introduced novel techniques to efficiently train models with a mixture of experts architecture, stabilizing performance and increasing GPU utilization.

DeepSeek's training efficiency stems from two main techniques: a mixture of experts model and a new method called MLA latent attention, which reduces both training and inference costs.

DeepSeek optimized its models for efficiency by using 8-bit floating point format for training, which significantly reduces memory usage without sacrificing performance.

The reasoning models being developed by DeepSeek are designed to utilize more compute, emphasizing the growing importance of inference in complex AI tasks.

DeepSeek's model architecture innovations, such as multi-head latent attention, dramatically reduce memory pressure, making their models more efficient.

DeepSeek's V3 model utilizes a mixture of experts architecture, activating only 37 billion out of 671 billion parameters for each token prediction, significantly reducing computation compared to models like Llama 3, which activates all 405 billion parameters.

DeepSeek's efficiency is highlighted by a high ratio of active experts, which necessitates splitting the model across different GPU nodes to manage complexity and prevent resource overload.

DeepSeek's V3 model, released in December, is a general-purpose model that performs comparably to other leading models like OpenAI's GPT-4 and Anthropic's Claude 3.5.