Author

Ming Lu

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Book Open access Sep 2026

BR-MoE: A Memory-Budget-Aware Co-Design Framework for Efficient MoE Inference on Resource-Constrained GPUs

BR-MoE, a memory-budget-aware co-design framework that treats per-expert bit-width and compensator rank as joint variables under a single memory constraint, establishes a superior memory–accuracy trade-off over state-of-the-art quantization methods, delivers substantial end-to-end inference speedups, and re-derives an...

Lei Jiang, Jing-Wei Sun, Jun-Qing Lin et al. · Proceedings of the Internati... · 0 citations

#large language models Book Open access Sep 2026

BR-MoE: A Memory-Budget-Aware Co-Design Framework for Efficient MoE Inference on Resource-Constrained GPUs

The Mixture-of-Experts (MoE) architecture has become the dominant paradigm for scaling Large Language Models, yet its explosive parameter growth routinely overflows the GPU memory budget. Weight quantization offers a practical way to fit these models into limited GPU memory, but reducing precision below 4 bits causes s...

Lei Jiang, Jingwei Sun, Jun-Qing Lin et al. · Proceedings of the Internati... · 0 citations

2025

Lua-LLM: Learning Unstructured-Sparsity Allocation for Large Language Models

Lua-LLM is proposed, a learning-based global pruning framework that explores the optimal unstructured sparsity allocation and achieves flexible allocation for both layer-wise and intra-layer sparsity.

Ming Lu, Jing-Wei Sun, Jun-Qing Lin et al. · Neural Information Processin... · 1 citation