SCBench: A KV Cache-Centric Analysis of Long-Context Methods
Authors: name: Yucheng Li, name: Huiqiang Jiang, name: Qianhui Wu, name: Xufang Luo, name: Surin Ahn, name: Chengruidong Zhang, name: Amir H. Abdi, name: Dongsheng Li, name: Jianfeng Gao, name: Yuqing Yang, name: Lili Qiu

Long-context LLMs have enabled numerous downstream applications but also
introduced significant challenges related to computational and memory
efficiency. To address these challenges, optimizations for long-context
inference have been developed, centered around the KV cache. However, existing
benchmarks often evaluate in single-request, neglecting the full lifecycle of
the KV cache in real-world use. This oversight is particularly critical, as KV
cache reuse has become widely adopted in LLMs inference frameworks, such as
vLLM and SGLang, as well as by LLM providers, including OpenAI, Microsoft,
Google, and Anthropic. To address this gap, we introduce
SCBench(SharedContextBench), a comprehensive benchmark for evaluating
long-context methods from a KV cachecentric perspective: 1) KV cache
generation, 2) KV cache compression, 3) KV cache retrieval, 4) KV cache
loading. Specifically, SCBench uses test examples with shared context, ranging
12 tasks with two shared context modes, covering four categories of
long-context capabilities: string retrieval, semantic retrieval, global
information, and multi-task. With it, we provide an extensive KV cache-centric
analysis of eight categories long-context solutions, including Gated Linear
RNNs, Mamba-Attention hybrids, and efficient methods such as sparse attention,
KV cache dropping, quantization, retrieval, loading, and prompt compression.
The evaluation is conducted on 8 long-context LLMs. Our findings show that
sub-O(n) memory methods suffer in multi-turn scenarios, while sparse encoding
with O(n) memory and sub-O(n^2) pre-filling computation perform robustly.
Dynamic sparsity yields more expressive KV caches than static patterns, and
layer-level sparsity in hybrid architectures reduces memory usage with strong
performance. Additionally, we identify attention distribution shift issues in
long-generation scenarios. https://aka.ms/SCBench.