English(EN) One 4k prompt costs a serving node as much as 900 generated tokens

Llama 3.1 70B的提示词处理成本是生成token时间的914倍

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-03 16:04

一项详细分析显示，在H100 SXM GPU上处理Llama 3.1 70B的4K token提示词，其资源消耗比普遍认为的要高得多，成本约相当于914个生成token。这种高成本源于预填充阶段，该阶段受计算能力限制，并且与上下文长度呈二次方增长，而解码阶段则受内存限制。当处理一个大型提示词时，它会暂停服务节点上所有其他活动对话，导致用户出现明显延迟。虽然分块预填充可以减少最大停顿时间，但并不会降低总计算成本。 AI

影响强调了处理长提示词的显著计算成本，影响了推理效率和用户体验。

排序理由对LLM服务成本和性能影响的分析，并非直接发布或产品公告。

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: Commentary; 对LLM服务成本和性能影响的分析，并非直接发布或产品公告。
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: 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]

dev.to — LLM tag TIER_1 English(EN) · Jax Sinclair · 2026-11-03 16:04

One 4k prompt costs a serving node as much as 900 generated tokens

<p>A node serving Llama-3.1-70B, 32 conversations streaming, GPU pegged at 95%. Then someone pastes a document. Every live conversation freezes for most of a second, then resumes. Nothing is broken and no config is wrong. The prefill is doing exactly what the arithmetic says it s…

本文如何被排名

报道来源 [1]

One 4k prompt costs a serving node as much as 900 generated tokens

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