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's recent models, including V3, highlight the importance of balancing training and inference compute resources to optimize performance and efficiency.

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.

DeepSeek's implementation of mixture of experts innovates by changing the routing mechanism, allowing for a high sparsity factor that activates only a small fraction of the model's parameters while ensuring all experts are utilized during training to improve model performance.

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

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

DeepSeek-V3 is a new mixture of experts transformer language model from DeepSeek in China, designed for instruction-based tasks similar to ChatGPT.

DeepSeek's R1 model builds on V3 by applying various algorithmic improvements to enhance its reasoning capabilities, achieving performance similar to OpenAI's models on complex reasoning benchmarks.