Author(s): Prachi rise

Originally published on Towards AI.

DeepSeek-V3: The Engineering Behind a Frontier Language Model — Part 1

This is the is full series of Deepseek-V3 technical report, where i explain all the technical details in simpler words with code implementation.

The article introduces DeepSeek-V3 as a Mixture of Experts (MoE) model and outlines key mechanisms used in its architecture, including Multi-head Latent Attention (MLA), Multi-token prediction (MTP), auxiliary-loss-free load balancing, FP8 mixed-precision training, large-scale pretraining, staged context-length extension, and post-training with SFT and RL. It then focuses on why MLA is used, explaining how KV-cache memory becomes a bottleneck during inference and how MLA reduces cache size by using low-rank joint compression to store a compact latent representation for keys and values, while maintaining a separate rotary component for positional information. The piece walks through the conceptual steps for forming queries, keys, and values, describes which representations need to be cached during autoregressive generation, and concludes with the core idea that MLA compresses content information into a shared latent space and separately caches compressed KV plus rotary keys to reduce memory overhead, followed by an implementation-oriented section with example code structure for the technique and a summary of “what we learned.”

Read the full blog for free on Medium.

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Published via Towards AI