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

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

DeepSeek's approach to training emphasizes compounding small improvements over time in data, architecture, and integration, which can lead to significant advancements.

A crucial enhancement in DeepSeek's models is the fp8 accumulation fix, which helps prevent small numerical errors from compounding during calculations, leading to more efficient training across thousands of GPUs. Additionally, DeepSeek needed to optimize GPU usage due to hardware constraints and export controls on GPU sales to China, as their GPUs were often idle, achieving only about 35% utilization.

DeepSeek's advancements indicate that there is potential for rebuilding the stack for optimizing GPU workloads, improving software at the inference layer, and developing AI-generated kernels, which is promising for AI applications in both consumer and B2B sectors.

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.