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Mixture of Quantized Experts (MoQE): Complementary Effect of Low-bit Quantization and Robustness
In our comprehensive analysis, we show that MoE models with 2-bit expert weights can deliver better model performance than the dense model trained on the same dataset.
FineQuant: Unlocking Efficiency with Fine-Grained Weight-Only Quantization for LLMs
Large Language Models (LLMs) have achieved state-of-the-art performance across various language tasks but pose challenges for practical deployment due to their substantial memory requirements.
Who Says Elephants Can't Run: Bringing Large Scale MoE Models into Cloud Scale Production
Mixture of Experts (MoE) models with conditional execution of sparsely activated layers have enabled training models with a much larger number of parameters.
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