English(EN) Your LLM Server Is Wasting 80% of Its GPU Memory — Here’s How vLLM Fixes That

KV 缓存优化解决 LLM GPU 内存瓶颈

作者 PulseAugur 编辑部 · [4 个来源] · 2026-05-18 22:35

大型语言模型 (LLM) 在服务效率方面面临着显著的瓶颈，原因是 KV 缓存的内存需求，它存储中间注意力计算。这个 KV 缓存对于实现更快的响应和处理更长的上下文窗口至关重要，但它会消耗高达 80% 的 GPU 内存。像 vLLM 的 PagedAttention 这样的创新，其灵感来自操作系统内存管理，通过优化 KV 缓存存储和减少内存碎片来解决这个问题，从而显著提高推理吞吐量。 AI

影响优化 KV 缓存和内存使用对于降低 LLM 服务成本和提高推理速度至关重要，从而能够更广泛地采用 AI 应用。

排序理由该集群讨论了 LLM 推理的技术优化和架构改进，特别关注 KV 缓存管理和内存效率，这与研究级别的技术内容相符。

在 Medium — MLOps tag 阅读 →

AI 生成摘要 · Google Gemini · 来自 4 个来源。我们如何撰写摘要 →

报道来源 [4]

X — SemiAnalysis TIER_1 English(EN) · SemiAnalysis_ · 2026-05-21 13:01

With modern agentic workloads and long context windows, a common bottleneck in serving LLMs at scale is where to store all the KV cache. Luckily, KV cache can b

With modern agentic workloads and long context windows, a common bottleneck in serving LLMs at scale is where to store all the KV cache. Luckily, KV cache can be extended beyond HBM into other tiers of memory. Nvidia uses the following naming convention to describe the tiers:
Medium — MLOps tag TIER_1 English(EN) · Tensormesh · 2026-05-21 21:07

KV Cache isn’t just Cache, it’s Memory: A Guide for LLM & Agent Devs

<div class="medium-feed-item"><p class="medium-feed-image"><a href="https://medium.com/@tensormesh/kv-cache-isnt-just-cache-it-s-memory-a-guide-for-llm-agent-devs-623a9974b5d5?source=rss------mlops-5"><img src="https://cdn-images-1.medium.com/max/2048/0*7mDkfDjKbG12mz6Y.png" widt…
Medium — MLOps tag TIER_1 English(EN) · Sumit Vedpathak · 2026-05-18 22:35

Your LLM Server Is Wasting 80% of Its GPU Memory — Here’s How vLLM Fixes That

<div class="medium-feed-item"><p class="medium-feed-image"><a href="https://pub.towardsai.net/your-llm-server-is-wasting-80-of-its-gpu-memory-heres-how-vllm-fixes-that-12d2fce99994?source=rss------mlops-5"><img src="https://cdn-images-1.medium.com/max/2600/1*H5dY_GD12nEVZ1470TWpM…
dev.to — LLM tag TIER_1 English(EN) · Kotcherla Murali Krishna · 2026-05-20 06:20

KV Cache Explained Like You're an LLM Engineer

<p>How transformer inference actually works under the hood — and why KV cache is the single most important optimization keeping your LLM from crawling.</p> <p>If you've ever wondered why LLMs respond fast even on long prompts — the answer is KV cache. But most explanations stop a…

报道来源 [4]

With modern agentic workloads and long context windows, a common bottleneck in serving LLMs at scale is where to store all the KV cache. Luckily, KV cache can b

KV Cache isn’t just Cache, it’s Memory: A Guide for LLM & Agent Devs

Your LLM Server Is Wasting 80% of Its GPU Memory — Here’s How vLLM Fixes That

KV Cache Explained Like You're an LLM Engineer

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