Towards Efficient Large Language Model Serving: A Survey on System-Aware KV Cache Optimization
arXiv:2607.08057v1 Announce Type: cross Abstract: Despite the rapid advancements of large language models (LLMs), LLM serving systems remain memory-intensive and costly. The key-value (KV) cache, which stores KV tensors during autoregressive decoding, is crucial for enabling low-latency, high-throughput LLM inference serving. In this survey, we focus on system-aware KV infrastructure for serving LLMs (abbreviated as sKis). We revisit recent work from a system behavior perspective, organizing existing efforts into three dimensions: execution and scheduling (temporal), placement and migration (s
The rapid and ongoing development of large language models is making their deployment and operational efficiency a critical bottleneck, driving intense research into optimization techniques for serving these models.
Efficient LLM serving systems are crucial for scaling AI applications, reducing operational costs, and democratizing access to advanced AI capabilities, directly impacting the economic viability and widespread adoption of LLMs.
This research focus suggests that the computational and memory demands of LLMs, while currently immense, are being systematically addressed, promising more cost-effective and performant AI deployments in the near future.
- · AI model developers
- · Cloud providers
- · Enterprises adopting LLMs
- · Hardware manufacturers (specialized memory/compute)
- · Inefficient LLM serving solutions
- · Companies without optimization expertise
Further reduction in the cost of running large language models, making advanced AI more accessible.
Increased competition among cloud providers on LLM inference costs and performance, driving innovation in system architecture.
Acceleration in the development and deployment of agentic AI systems and other complex LLM applications as serving constraints diminish.
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