Published on June 2, 2026
Mixture-of-Experts (MoE) large language models have long provided a solution to reduce computational load through sparse activation of expert networks. Despite their efficiency, deploying these models has faced challenges due to high memory requirements, as all expert weights must remain in memory. This situation limited their practicality in ultra-low-bit scenarios.
Recent advancements reveal that existing MoE compression techniques have fallen short in addressing these limitations. Pruning methods often lead to a permanent loss of model capacity, while conventional quantization strategies struggle to optimally allocate bit resources across the heterogeneous significance of model components. Enter BitsMoE, a new framework designed to tackle these issues a spectral-energy-guided bit allocation approach.
BitsMoE innovatively decomposes MoE layers using singular value decomposition (SVD), which separates shared and expert-specific information. This process preserves integral structures while enabling targeted, fine-grained quantization for each expert. integer linear program, BitsMoE effectively minimizes reconstruction loss while adhering to set bit budgets, enhancing overall performance.
The impact is significant, as BitsMoE demonstrates impressive results across various MoE LLMs. In a recent test with the Qwen3-30B-A3B-Base model, it accelerated quantization by 12.3 times, improved accuracy 28 percentage points, and increased decoding speed significantly over existing methods. This advancement marks a crucial step forward in the usability and efficiency of MoE models in computational applications.
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