BitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization

• URL: http://arxiv.org/abs/2606.00079v1
• Date: Fri, 22 May 2026 13:05:53 GMT
• Title: BitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization
• Abstract summary: Mixture-of-Experts (MoE) large language models reduce per-token computation through sparse expert activation.<n>Existing MoE compression methods struggle in the ultra-low-bit regime.<n>We propose BitsMoE, a spectral-energy-guided bit-allocation framework for MoE LLM quantization.
• Score: 5.878850231726241
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mixture-of-Experts (MoE) large language models reduce per-token computation through sparse expert activation, but their deployment remains memory-intensive because all expert weights must be kept resident in memory. Existing MoE compression methods struggle in the ultra-low-bit regime: pruning irreversibly removes model capacity, while coarse-grained quantization fails to allocate bits according to heterogeneous expert and weight-direction importance. We propose BitsMoE, a spectral-energy-guided bit-allocation framework for MoE LLM quantization. BitsMoE decomposes each MoE layer by SVD into a shared basis and expert-specific spectral factors, retaining the shared basis without quantization to preserve common cross-expert structure and using the expert-specific factors as fine-grained quantization units. To determine the bit-width of each unit, BitsMoE formulates spectrum-wise mixed-precision quantization as an activation-aware reconstruction surrogate and solves an integer linear program that minimizes estimated reconstruction loss under a fixed bit budget. Experiments across multiple MoE LLMs show that BitsMoE substantially reduces downstream task accuracy degradation in ultra-low-bit regimes. Under 2-bit quantization on Qwen3-30B-A3B-Base, BitsMoE accelerates quantization by 12.3$\times$, improves average accuracy by 27.83 percentage points, and increases decoding speed by 1.76$\times$ over GPTQ. Our model and code are publicly available at https://github.com/zjiayu064/BitsMoE.

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