DeepSeek v3: Significant Improvements to the Transformer Architecture
2025-01-28
DeepSeek v3 achieves state-of-the-art benchmark performance with significantly less compute than comparable models. This is due to key architectural improvements: Multi-head Latent Attention (MLA) drastically reduces KV cache size without sacrificing model quality; improved Mixture-of-Experts (MoE) tackles routing collapse via auxiliary-loss-free load balancing and shared experts; and multi-token prediction boosts training efficiency and inference speed. These improvements demonstrate a deep understanding of the Transformer architecture and point the way forward for large language models.
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AI