DeepSeek has begun IPO preparation targeting a 2027 listing, possibly as early as year‑end, backed by a $1.5 billion financing round that lifts its valuation to $71 billion, while its founder retains roughly 78% of equity and the company showcases a self‑developed, cost‑efficient AI stack.
This article provides a detailed technical analysis of DeepSeek‑V3, covering its MOE architecture, the novel Multi‑head Latent Attention (MLA) mechanism, the DualPipe pipeline‑parallel algorithm, mixed‑precision FP8 training, and the Multi‑Token Prediction (MTP) inference improvements that together boost performance and efficiency.
This article explains how the DualPipe scheduling mechanism in DeepSeek‑R1 improves GPU cluster compute‑communication efficiency by using fine‑grained pipeline stages and bidirectional data flow, comparing it with Zero Bubble pipeline parallelism and discussing the challenges of large‑scale distributed training.
