arXiv:2510.02361v2 Announce Type: replace Abstract: Transformer-based large models excel in natural language processing and computer vision, but face severe computational inefficiencies due to the self-attention's quadratic complexity with input tokens. Recently, researchers have proposed a series of methods based on block selection and compression to alleviate this problem, but they either have issues with semantic incompleteness or poor training-inference efficiency. To comprehensively address these challenges, we propose ChunkLLM, a lightweight and pluggable training framework. Specifically
Ongoing advancements in AI research continually address computational bottlenecks to improve model efficiency and accessibility, making this development timely.
Improving LLM inference efficiency reduces computational costs and broadens the deployment possibilities for advanced AI, impacting various industries.
New pluggable frameworks like ChunkLLM could make high-performance LLMs more resource-efficient and easier to integrate into diverse systems without extensive retraining.
- · AI developers
- · Cloud computing providers
- · Businesses adopting LLMs
- · Hardware manufacturers
- · Companies with less efficient LLM architectures
- · High-cost, specialized AI hardware requiring specific model structures
More widespread and cost-effective deployment of powerful large language models.
Accelerated development of new AI applications and services due to reduced operational friction.
Increased competition in AI markets as barriers to entry for advanced model utilization are lowered.
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