arXiv:2606.09885v1 Announce Type: new Abstract: Mixture-of-Experts large language models (LLMs) scale efficiently through sparse activation, yet their deployment is fundamentally constrained by the large static parameter footprint of experts. Existing compression approaches either remove entire experts, disrupting routing topology and harming performance, or rely on unstructured weight pruning with limited practical efficiency. To address the limitations, we propose TENP, a structured Trapezoidal ExpertNeuron Pruning framework. Using a few samples, we identify and retain important experts, whi
The proliferation of Mixture-of-Experts (MoE) models in large language models necessitates continued innovation in efficiency, leading to new pruning techniques to optimize their deployment.
Improving the efficiency of MoE LLMs by reducing their parameter footprint without sacrificing performance is critical for broader adoption and sustainable scaling.
New structured pruning methods like TENP will allow for more practical deployment of large MoE models on diverse hardware, enabling greater accessibility and reducing operational costs.
- · AI developers
- · Cloud providers
- · Edge AI hardware manufacturers
- · Enterprises adopting LLMs
- · Legacy unstructured pruning methods
- · Less efficient LLM architectures
- · Data centers with constrained power/cooling
Increased practical deployment of MoE LLMs across various platforms.
Reduced computational costs and energy consumption for large-scale AI applications.
Acceleration of AI research and development due to more accessible and efficient models, potentially leading to new breakthroughs.
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Read at arXiv cs.LG