DeepSeek's technique is not a universal solution for all AI agents to run twice as fast, but it is highly effective in challenging situations like long conversations with large amounts of data.

DeepSeek's AI models perform as efficiently as their American counterparts.

DeepSeek scientists solved the AI inefficiency problem by rerouting data to underutilized decoding machines, creating a second path to the prefill machines.

The primary application for DeepSeek's technique is long multi-turn agentic workloads.

DeepSeek makes money by selling services to developers at a fraction of OpenAI's prices.

Compressed Sparse Attention (CSA) in Deepseek V4 groups small chunks of tokens, such as four at a time, into a single denser representation to reduce sequence length.