Key-Value Cache and Memory in Large Language Models

Large Language Models (LLMs) have become increasingly popular in recent years due to their ability to process and understand human language. However, as the size of LLMs continues to grow, the need for efficient memory management becomes more pressing. One crucial component of LLMs is the Key-Value (KV) cache, which is responsible for storing and retrieving key-value pairs. In this paper, we will explore the importance of KV cache and memory in LLMs, and discuss various techniques for optimizing their performance.

Importance of KV Cache in LLMs

The KV cache is a critical component of LLMs, as it enables the model to efficiently store and retrieve key-value pairs. The KV cache is used to store the output of the attention mechanism, which is a key component of LLMs. The attention mechanism is used to compute the weighted sum of the input vectors, and the KV cache is used to store the weights and values of the attention mechanism.

Memory Requirements of LLMs

LLMs require a significant amount of memory to store the model's parameters, input data, and intermediate results. The memory requirements of LLMs can be broken down into several components, including:

1. Model parameters: The model parameters are the weights and biases of the LLM, which are used to compute the output of the model.

2. Input data: The input data is the text or other data that is fed into the LLM.

3. Intermediate results: The intermediate results are the outputs of the various layers of the LLM, which are used to compute the final output of the model.

Techniques for Optimizing KV Cache and Memory in LLMs

There are several techniques that can be used to optimize the KV cache and memory in LLMs, including:

1. Compressed Context Memory: This technique involves compressing the context memory of the LLM, which can reduce the memory requirements of the model.

2. Adaptive KV Cache Size Allocation: This technique involves dynamically allocating the size of the KV cache based on the input data and the model's parameters.

3. PyramidInfer: This technique involves using a pyramid-shaped inference architecture to reduce the memory requirements of the LLM.

Conclusion

In conclusion, the KV cache and memory are critical components of LLMs, and optimizing their performance is essential for achieving efficient and accurate results. By using techniques such as compressed context memory, adaptive KV cache size allocation, and PyramidInfer, it is possible to reduce the memory requirements of LLMs and improve their performance.

Sources & References

  • ICLR 2026 - Submissions: The Key-Value (KV) cache is crucial for efficient Large Language Models (LLMs) inference, but excessively long contexts drastically increase KV cache memory
  • Personalized KV Cache Memory Reduction for Long-Context LLM...: At the end of mini-prefill, these attention score distribution vectors for all layers are used to derive a globally optimal KV cache size allocation list through an adaptive KV cache size allocation algorithm.
  • Zefan-Cai/Awesome-LLM-KV-Cache - GitHub: | 2023.12 | Compressed Context Memory For Online Language Model Interaction | | | Finetuning LLMs to recurrently compress KV caches |. | 2024.05 | PyramidInfer: PyramidInfer: PyramidInfer is a novel inference architecture that uses a pyramid-shaped architecture to reduce the memory requirements of LLMs.
  • Scraper Spider: Keywords: #granite33:8b, 'Sort Of Works', AGI Embers, AIs, Abstraction Levels, Abstractions, Accountability, Agent Behavior Reasoning, Agent Complexity, Agent Stacking, Agentic Execution, Agentic Gap,
  • Computer Science - arXiv: We introduce CAT, a framework designed to evaluate and visualize the interplay of accuracy and response consistency of Large Language Models (LLMs) under controllable input conditions.
  • Personalized KV Cache Memory Reduction for Long-Context LLM...: At the end of mini-prefill, these attention score distribution vectors for all layers are used to derive a globally optimal KV cache size allocation list through an adaptive KV cache size allocation algorithm
  • Zefan-Cai/Awesome-LLM-KV-Cache - GitHub: | 2023.12 | Compressed Context Memory For Online Language Model Interaction | | | Finetuning LLMs to recurrently compress KV caches |. | 2024.05 | PyramidInfer: PyramidInfer: PyramidInfer is a novel inference architecture that uses a pyramid-shaped architecture to reduce the memory requirements of LLMs
  • Scraper Spider: Keywords: #granite33:8b, 'Sort Of Works', AGI Embers, AIs, Abstraction Levels, Abstractions, Accountability, Agent Behavior Reasoning, Agent Complexity, Agent Stacking, Agentic Execution, Agentic Gap
  • Computer Science - arXiv: We introduce CAT, a framework designed to evaluate and visualize the interplay of accuracy and response consistency of Large Language Models (LLMs) under controllable input conditions
  • Sources: ICLR 2026 - Submissions, Personalized KV Cache Memory R, Zefan-Cai/Awesome-LLM-KV-Cache
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