English(EN) Should you run an LLM locally or in the cloud?

LLM 部署：本地与云端的成本效益分析

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-26 20:34

当模型能够容纳在机器内存中且数据隐私至关重要时，在本地运行大型语言模型是可行的。对于超出本地容量的更大模型或批量作业，基于云的解决方案更为合适。通过比较硬件成本与云端每小时费率，可以量化本地硬件与云服务之间的选择，在持续使用几年后，本地拥有权会更具成本效益。 AI

影响为 AI 运营者提供关于根据模型大小和使用模式优化 LLM 部署成本和性能的指导。

排序理由文章讨论了在本地与云端运行 LLM 的权衡和成本效益分析，而不是发布新模型或产品。

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 6 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Commentary; 文章讨论了在本地与云端运行 LLM 的权衡和成本效益分析，而不是发布新模型或产品。
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: infra, product; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

dev.to — LLM tag TIER_1 English(EN) · Viswaretas Kotra · 2026-10-26 20:34

Should you run an LLM locally or in the cloud?

<p>Run an LLM locally when the model fits comfortably in your machine's memory and your data should never leave it; move to the cloud when the model, the fine-tune or the batch job outgrows that memory. Nodus runs those bigger jobs on the GPU they actually need, caps each run at …