English(EN) How much VRAM do popular LLMs really need at 4-bit and 8K context? Data from 26 models

LLM VRAM需求：真实数据揭示8K上下文下的KV缓存影响

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 03:59

一项新的分析详细介绍了运行26个流行的开源LLM在4位精度和8K上下文窗口下的实际VRAM需求。研究发现，虽然权重通常占内存使用的大部分，但KV缓存的大小对于具有长上下文的模型来说可能变得很重要，特别是那些架构不限制缓存增长的模型。这些数据来自GGUF文件大小和模型配置，可供社区公开使用。 AI

影响为本地LLM部署提供了关键数据，帮助用户根据特定的模型架构和上下文长度优化硬件选择。

排序理由该条目详细介绍了LLM技术规格的实证数据收集和分析，类似于一项研究。[lever_c_demoted from research: ic=1 ai=1.0]

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dev.to — LLM tag TIER_1 English(EN) · Gary Nagra · 2026-11-02 03:59

How much VRAM do popular LLMs really need at 4-bit and 8K context? Data from 26 models

<p>"Will it fit on my card?" comes up constantly in local-LLM threads, and the usual answer is a rule of thumb like "about half a gigabyte per billion parameters at 4-bit." That rule is close enough for small dense models. For mixture-of-experts models, models with sliding-window…