arXiv:2606.29563v1 Announce Type: cross Abstract: Large language models (LLMs) excel at complex tasks like question answering and summarization, thanks to their ability to handle long-context inputs. However, deploying LLMs is costly, not only due to the high computational demands of quadratic complexity of self-attention and auto-regressive generation, but also because of the significant memory overhead required for storing the key-value (KV) cache during inference. To reduce the memory cost, existing KV-cache eviction strategies leverage the sparsity in attention to selectively store a subse
The continuous growth in LLM complexity and adoption is driving an urgent need for more efficient inference mechanisms to manage rising computational costs and memory overhead.
Improved KV cache eviction strategies directly address the significant memory bottleneck and high operational costs associated with large language models, making their deployment more sustainable and scalable.
This advancement changes the economic viability of deploying larger LLMs by reducing memory footprints and computational demands, allowing for more efficient inference and broader application.
- · LLM developers
- · Cloud providers
- · AI-powered services
- · End-users of LLMs
- · Inefficient inference solutions
- · Organizations with high LLM operational costs
Reduced inference costs enable more widespread deployment and higher utilization of advanced LLMs.
The ability to run more complex models cost-effectively could accelerate innovation in AI applications and services.
Increased LLM accessibility and reduced operational barriers might contribute to an earlier proliferation of AI agents and sophisticated AI-driven systems.
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Read at arXiv cs.AI