DeepSeek's model is open-weight, meaning its model weights are available for public download, allowing users to run the model independently and control their data.

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

DeepSeek's reasoning model was released for free at scale, contrasting with other models that required paid subscriptions, which contributed to its rapid adoption.

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 recent achievements in AI are notable, but their models are limited to text, while Llama 4 models are competitive, offering lower cost-per-intelligence for text tasks and leading in multimodal capabilities, indicating a potential gap in DeepSeek's capabilities.

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 R1 is an open-source reasoning model that claims to achieve performance comparable to OpenAI's models at a significantly lower cost, causing a stir in the AI community.

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

DeepSeek has a shocking $5 million training cost, highlighting the disparity in resources available to different entities for developing AI models.