TileQ: Efficient Low-Rank Quantization of Mixture-of-Experts with 2D Tiling

• URL: http://arxiv.org/abs/2605.09281v1
• Date: Sun, 10 May 2026 03:10:20 GMT
• Title: TileQ: Efficient Low-Rank Quantization of Mixture-of-Experts with 2D Tiling
• Abstract summary: Mixture-of-Experts (MoE) models achieve remarkable performance by sparsely activating specialized experts.<n>Low-rank quantization offers a promising route to compress MoE models, but existing methods still incur nonnegligible memory overhead and inference latency.<n>We propose textscTileQ, a fine-tuning-free post-training quantization to share low-rank factors across both input and output dimensions of MoE experts.
• Score: 3.4199566861263135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mixture-of-Experts (MoE) models achieve remarkable performance by sparsely activating specialized experts, yet their massive parameters in experts pose significant challenges for deployment. While low-rank quantization offers a promising route to compress MoE models, existing methods still incur nonnegligible memory overhead and inference latency. To address these limitations, we propose \textsc{TileQ}, a fine-tuning-free post-training quantization (PTQ) method that employs 2D-tiling structured low-rank quantization to share low-rank factors across both input and output dimensions of MoE experts. Furthermore, we introduce an efficient inference technique for \textsc{TileQ} that fuses multiple low-rank expert computations into a single-pass operation, significantly improving hardware utilization. Experiments show that \textsc{TileQ} cuts down additional memory usage up to 10$\times$ and reduces inference latency to $\sim$5\% while preserving state-of-the-art accuracy.

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