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English(EN) First few days of qwen3.8-flash-next on 4x R9700 - it's been really interesting so far

Qwen3.8-Flash-Next 模型在早期用户测试中以速度和质量令人印象深刻

一位 Reddit 用户分享了他们对 Qwen3.8-Flash-Next 模型的积极早期体验,指出其在代理编码任务方面速度和质量令人印象深刻。在四块 R9700 GPU 上运行,该模型实现了约 100 tokens/秒 的并发流生成速度和超过 150 tokens/秒 的单流生成速度,预填充速度超过 10,000 tokens/秒。用户对该模型的性能感到惊讶,特别是考虑到其效率。 AI

影响 展示了在本地部署 LLM 的强大性能,可能提高编码任务的效率。

排序理由 在消费级硬件上对特定模型版本的用户测试。

在 r/LocalLLaMA 阅读 →

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

Qwen3.8-Flash-Next 模型在早期用户测试中以速度和质量令人印象深刻

本文如何被排名

Signal score
0 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
在消费级硬件上对特定模型版本的用户测试。
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, 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
1 days old
Coverage has settled into its steady-state source set.

完整方法见我们的编辑标准。

报道来源 [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/pubudeux ·

    qwen3.8-flash-next 在 4x R9700 上运行的最初几天——到目前为止真的很有趣

    <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1wsxgbo/first_few_days_of_qwen38flashnext_on_4x_r9700_its/"> <img alt="First few days of qwen3.8-flash-next on 4x R9700 - it's been really interesting so far" src="https://preview.redd.it/n17r7tw87dsh1.png?wid…