Tsinghua and Z.ai unveil IndexCache to speed long-context DeepSeek-style sparse attention inference by up to 1.82x

Researchers at Tsinghua University and Z.ai developed IndexCache, an optimizer for DeepSeek Sparse Attention models that reduces redundant computation during long-context inference. In tests with 200,000-token contexts, it delivered up to 1.82x faster time-to-first-token and 1.48x higher generation throughput, including early validation on the 744B-parameter GLM-5 model.

IndexCache targets one of the biggest production bottlenecks for long-context LLMs: the cost of repeatedly scoring large token histories. If broadly adopted, optimizations like this could make long-context AI more practical for enterprise document analysis, agent workflows, and reasoning-heavy applications without requiring entirely new model architectures.