CompressKV: Semantic-Retrieval-Guided KV-Cache Compression for Resource-Efficient Long-Context LLM Inference
arXiv:2606.24467v1 Announce Type: new Abstract: Long-context large language model (LLM) inference is increasingly constrained by the memory footprint and decoding cost of key-value (KV) caches, limiting sustainable deployment on resource-constrained hardware. Existing KV cache eviction methods typically apply heuristic token scoring over all heads in GQA-based LLMs. These methods ignore the different functionalities of attention heads, leading to the eviction of critical tokens and thus degrading the performance of LLMs. To address this issue, we propose CompressKV, a resource-efficient KV-cac
Rapid advancements in LLM capabilities are increasingly bottlenecked by hardware resource constraints, making efficient memory management a critical focus for broader deployment.
Efficient KV-cache management enables more resource-efficient and scalable LLM inference, addressing a key limitation for deploying powerful models on diverse hardware environments.
Existing heuristic-based KV cache eviction methods will be superseded by more semantically aware compression techniques, leading to improved LLM performance and accessibility.
- · LLM developers and researchers
- · Cloud providers
- · Edge AI hardware manufacturers
- · Companies deploying long-context LLMs
- · Inefficient KV cache methods
- · Hardware manufacturers relying solely on brute-force memory scaling
Reduced operational costs and energy consumption for LLM inference due to optimized memory usage.
Expansion of LLM inference capabilities to more resource-constrained devices, such as mobile or embedded systems.
Acceleration of AI agent development and deployment on edge devices, fostering a more ubiquitous AI landscape.
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Read at arXiv cs.AI