随着 LLM 向 1M 上下文演进，KV cache（键值缓存）已成为制约推理服务效率的核心瓶颈。自回归生成的特性使得模型必须存储历史 token 的 key-value 状态（即 KV cache）以避免重复计算，但 KV cache 的显存占用随着上下文长度的增长而膨胀，带来显著的内存瓶颈。

在生产环境部署过LLM的人都知道模型权重只是问题的一半，另一半是KV cache：存储注意力状态的运行时内存，让模型在生成token时不必从头开始重算。能不能管好这块内存决定了系统是一个卡顿的demo还是一个可用的推理服务。 本文梳理KV cache管理经历的5个时代 ...

2026年3月，谷歌研究院发布TurboQuant压缩算法技术，迅速在存储与AI基础设施领域引发热议。该算法能够压缩KV缓存，实现内存占用降低6倍、推理速度提升8倍的潜力。这一技术突破的背后，折射出大模型推理时代最核心的硬件瓶颈：KV Cache正成为制约AI部署规模的 ...

Running a large language model is expensive, and a surprising amount of that cost comes down to memory, not computation.

Within 24 hours of the release, community members began porting the algorithm to popular local AI libraries like MLX for Apple Silicon and llama.cpp.

你输入个几百字，它输出就得慢慢挤牙膏。 是模型本身算力不够吗？ 不全是。 这里面其实藏着一个非常基础的效率问题，而解决这个问题的核心技术，就是今天要跟大家聊明白的 KV Cache。 1. 先铺垫一下：这些基础术语你得懂 聊KV Cache之前，得先把一些最基础的 ...

A team of Google researchers has published a technique that could let developers squeeze roughly three times more throughput ...

Google's TurboQuant reduces the KV cache of large language models to 3 bits. Accuracy is said to remain, speed to multiply. Google Research has published new technical details about its compression ...

Large language models (LLMs) aren’t actually giant computer brains. Instead, they are massive vector spaces in which the probabilities of tokens occurring in a specific order is encoded. Billions of ...