arXiv:2512.12744v4 Announce Type: replace Abstract: Activation sparsity offers a compelling route to accelerate large language model (LLM) inference by selectively suppressing hidden activations, yet existing approaches exhibit severe accuracy degradation at high sparsity. We show that this failure stems from representational instability: *activation sparsity disrupts input-dependent activation learned during pretraining, inducing distribution shifts in hidden states.* We address this issue by reframing activation sparsity as a representational alignment problem and introducing **Spontaneous N
The increasing computational demands of LLMs necessitate more efficient inference methods, making robust activation sparsity a critical area of research at this stage of AI development.
Improved activation sparsity can significantly accelerate LLM inference, reducing the energy and computational costs associated with large-scale AI deployment and potentially enabling wider adoption.
This research provides a method to overcome significant accuracy degradation at high sparsity, potentially making sparse LLM inference a viable and widely applicable technique without compromising model performance.
- · AI infrastructure providers
- · Cloud computing services
- · LLM developers
- · AI hardware manufacturers
- · Companies with inefficient LLM deployments
- · Traditional dense model architectures
More efficient LLM deployment and operation due to reduced computational requirements.
Lower barriers to entry for using advanced LLMs, fostering innovation and new applications.
Potential for new specialized AI hardware optimized for sparse activation patterns, driving further hardware-software co-design.
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Read at arXiv cs.LG