TTKV: Temporal-Tiered KV Cache for Long-Context LLM Inference

📰 ArXiv cs.AI

Learn how TTKV caching improves long-context LLM inference by leveraging temporal-tiered key-value caching, and why it matters for efficient language model performance

advanced Published 23 Apr 2026

Action Steps

Implement a temporal-tiered KV cache using TTKV to reduce memory footprint
Configure the cache to prioritize recent and frequently accessed KV states
Test the TTKV cache with varying context lengths to evaluate its performance
Compare the results with existing KV caching approaches to assess the improvement
Apply the TTKV cache to real-world LLM inference tasks to demonstrate its effectiveness

Who Needs to Know This

ML engineers and researchers working on large language models can benefit from this technique to improve inference efficiency and scalability

Key Insight

💡 TTKV caching leverages the non-uniform importance of KV states over time to reduce memory footprint and improve inference efficiency

Key Takeaways

Learn how TTKV caching improves long-context LLM inference by leveraging temporal-tiered key-value caching, and why it matters for efficient language model performance

Full Article

Title: TTKV: Temporal-Tiered KV Cache for Long-Context LLM Inference

Abstract:
arXiv:2604.19769v1 Announce Type: cross Abstract: Key-value (KV) caching is critical for efficient inference in large language models (LLMs), yet its memory footprint scales linearly with context length, resulting in a severe scalability bottleneck. Existing approaches largely treat KV states as equally important across time, implicitly assuming uniform precision and accessibility. However, this assumption contrasts with human memory systems, where memories vary in clarity, recall frequency, and

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