用户正在探索两个开源大语言模型 Olmo 和 Apertus，它们以使用开放数据训练而闻名。他们尝试在本地运行这些模型时遇到了内存分配错误，这表明需要大量的硬件资源。 AI
影响 凸显了在本地运行先进的开源大语言模型所需的巨大硬件资源。
排序理由 关于在本地运行现有模型的用户体验报告，并非新发布或重大的行业事件。
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用户在本地运行开源大语言模型 Olmo 和 Apertus 时遇到内存问题
本文如何被排名
- Signal score
- 14 / 100
- Composite score across the factors below. Higher = stronger signal that this story matters right now.
- Newsworthiness bucket
- Tool
- 关于在本地运行现有模型的用户体验报告，并非新发布或重大的行业事件。
- 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
- model release, infra
- 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。
报道来源 [1]
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我一直在关注 Olmo 和 Apertus，据我所知，它们是在真正开放的数据上训练的 LLM，当然我也想在本地运行它们。然而
So I've been having a look at Olmo and Apertus, which AFAICT are LLMs trained on actually open data, and of course I would also like to run them locally. However... Loading model... ggml_aligned_malloc: insufficient memory (attempted to allocate 32768,00 MB) Meh. Might need to se…