The traditional large language models (LLMs) were perceived to be stagnating, but DeepSeek's release has injected new life into the field.

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

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

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.

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

The rapid rise of DeepSeek has raised questions about how they achieved such compatibility and capabilities with existing models.

DeepSeek's R1 model is one of the first large models to achieve top-tier results purely through reinforcement learning, marking a significant milestone in AI development. Additionally, DeepSeek introduced a cold start phase for fine-tuning on structured reasoning examples before reinforcement learning, which eliminated language mixing issues and made outputs far more comprehensible.

DeepSeek-R1 is a reasoning model that builds on the training of DeepSeek-V3, enhancing discussions around AI capabilities and accelerating industry conversations.