Unverified50% confidenceFactExact time
Mixtral 8x7B has a total of approximately 46.7 billion parameters, but only 2 experts are activated per inference, making the actual computation equivalent to a model of approximately 13 billion parameters.
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Long-term
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9/6/2026
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UnverifiedMixtral 8x7B has a total of approximately 46.7B parameters, activates only approximately 12.9B parameters per forward pass, and reduces inference computation by approximately 60% compared to dense models of equivalent performance.73% similarUnverifiedMixtral 8x7B等MoE模型实现了接近70B模型的效果，却只需约13B模型的推理成本72% similarVerifiedMistral's Mixtral 8x7B model uses a sparse activation mechanism that activates only 2 of 8 experts per inference, achieving performance close to Llama 2 70B at the computational cost of approximately 13B parameters.70% similarUnverified根据DeepMind在2022年提出的Chinchilla缩放定律，一个700亿参数的模型需要约1.4万亿token才能达到计算最优70% similarUnverifiedM2.5 adopts a Mixture of Experts (MoE) architecture, where only 10B parameters are actually involved in computation during each inference, while the total parameter count may far exceed 10B.69% similar
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