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.

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

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

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