DeepSeek v3, a state-of-the-art open-weight large language model, achieves superior benchmark performance using significantly less training compute than comparable models. This efficiency stems from architectural improvements detailed in a technical report, notably multi-head latent attention (MLA) which reduces key-value cache size without sacrificing quality, and refined mixture-of-experts (MoE) techniques that mitigate routing collapse through bias adjustments and shared experts. Furthermore, multi-token prediction enhances both training and inference speed. The article analyzes these innovations, explaining their mechanisms and impact on Transformer architecture.

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