- Updated: January 30, 2026
- 1 min read
Understanding Bottlenecks for Efficiently Serving LLM Inference With KV Offloading – A Deep Dive
Understanding Bottlenecks for Efficiently Serving LLM Inference With KV Offloading
KV cache offloading enables long‑context large language model (LLM) inference by storing caches in CPU DRAM. However, the limited PCIe bandwidth creates severe performance bottlenecks. In this article we explore the analytical framework introduced in the recent arXiv paper Understanding Bottlenecks for Efficiently Serving LLM Inference With KV Offloading, which derives the critical cached‑to‑prefill token ratio \(\kappa_{\text{crit}}\) where execution becomes memory‑bound.
The study shows that typical workloads exceed this threshold by orders of magnitude, with 99% of latency spent on data transfers. Consequently, GPUs operate at only ~28% of their rated TDP, highlighting the need for optimized hardware interconnects, model architectures, and scheduling algorithms.
For a deeper look at the KV offloading architecture and our proposed optimizations, visit our KV Offloading page. Explore more related articles on our blog.
Andrii Bidochko
CTO UBOS
Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.