English(EN) Your model card says 128k. The machine serving it picked 4k.

Ollama 的上下文长度由 VRAM 决定，而非仅由模型文件决定

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-03 02:07

由 Ollama 服务的 LLM 的有效上下文窗口并非仅由模型文件决定，还受到服务机器可用 VRAM 的影响。如果机器的 VRAM（在扣除开销后）低于特定阈值（例如 23 GiB），Ollama 将默认使用 4096 个 token 的上下文长度。此默认值仅在用户、模型选项或环境变量未明确提供上下文长度时应用。模型文件本身只能向下限制上下文窗口，而不能设置实际的操作限制。 AI

影响阐明了硬件限制如何在部署的系统中限制 LLM 的上下文窗口大小，从而影响性能和可用性。

排序理由对特定软件（Ollama）的配置如何根据硬件资源影响 LLM 上下文窗口大小的技术解释。

在 dev.to — LLM tag 阅读 →

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本文如何被排名

Signal score: 5 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 对特定软件（Ollama）的配置如何根据硬件资源影响 LLM 上下文窗口大小的技术解释。
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; 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: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

dev.to — LLM tag TIER_1 English(EN) · pm25coder · 2026-11-03 02:07

Your model card says 128k. The machine serving it picked 4k.

<p>I got this one wrong first, and the reason is worth more than the correction.</p> <p>I assumed the effective context window was a property of the model file. Dump the GGUF header, read <code>context_length</code>, compare it against what the model card claims, done. That check…

本文如何被排名

报道来源 [1]

Your model card says 128k. The machine serving it picked 4k.

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