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MoE (Mixture of Experts) architecture

MoE (Mixture of Experts) architecture is a neural network design architecture that partitions parameters into specialized sub-networks, known as "experts," and activates only a relevant subset of them for each input token or task.^[001-TODO__Gemma_4_26B_本地AI模型深度解析.md]

This approach allows models to maintain a high total parameter count (providing greater knowledge capacity and capability) while keeping the computational cost and inference speed comparable to a much smaller, dense model^[001-TODO__Gemma_4_26B_本地AI模型深度解析.md]。

Comparison with Dense Models

The fundamental difference between MoE and traditional Dense Models lies in how they utilize their parameters^[001-TODO__Gemma_4_26B_本地AI模型深度解析.md]：

  • Dense Models: In a dense architecture (e.g., a traditional 26B parameter model), all parameters participate in the calculation for every single inference step. This is analogous to a meeting where the "entire staff" is present for every decision^[001-TODO__Gemma_4_26B_本地AI模型深度解析.md]。
  • MoE Models: The total parameters are divided into multiple specialized "expert" departments. For any given input or problem, the system only wakes up the specific relevant experts to handle it^[001-TODO__Gemma_4_26B_本地AI模型深度解析.md]。 It is estimated that in some implementations, only 1/4 of the parameters (or even fewer) are actually active during a single forward pass^[001-TODO__Gemma_4_26B_本地AI模型深度解析.md]。

Performance and Efficiency

The primary advantage of the MoE architecture is the decoupling of model size from inference speed^[001-TODO__Gemma_4_26B_本地AI模型深度解析.md]。

  • Speed: Because only a fraction of the total parameters are computed per token, MoE models can achieve inference speeds significantly higher than dense models of the same total size. The Gemma 4 26B model, for example, is reported to be approximately 5 times faster than traditional models of a similar class^[001-TODO__Gemma_4_26B_本地AI模型深度解析.md]。
  • Resource Efficiency: This efficiency allows for running very capable models on consumer-grade hardware. A model like Gemma 4 26B can run on a single 24GB graphics card (e.g., RTX 3090), whereas a dense 26B model would typically require substantially more VRAM or compute power to achieve similar throughput^[001-TODO__Gemma_4_26B_本地AI模型深度解析.md]。
  • [[Quantization]]
  • [[KV Cache]]
  • [[Gemma 4 26B]]

Sources

  • 001-TODO__Gemma_4_26B_本地AI模型深度解析.md