competitive with industry leaders like GPT-4 and Claude 3 Opus, but with a radically different and more efficient architecture.Architectural Innovation: The DeepSeek-V2 BreakthroughThe unveiling of DeepSeek-V2 was a landmark moment, showcasing technical ingenuity that addressed two of the biggest hurdles in LLMs: training cost and inference cost.Mixture of Experts (MoE): At its heart, DeepSeek-V2 employs a sophisticated MoE architecture. Unlike a “dense” model where all parameters are activated for every query, an MoE model has a network of “expert” sub-networks. For any given input, a smart routing mechanism activates only a fraction of these experts (e.g., 2.4% of the model’s total 236 billion parameters). This dramatically reduces computational load during inference, making the model far cheaper and faster to run.The Innovator’s Twist – Multi-head Latent Attention (MLA): DeepSeek introduced a novel attention mechanism to complement its MoE
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