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English(EN) Qwen3.8-Next streaming - 150tps prefill, 3.6 tps decode on M5 Air

Qwen3.8-Next 模型在 M5 硬件上实现更快性能

一位用户已成功将其流式传输堆栈适配到运行 Qwen3.8-Next 模型,在其 M5 硬件上实现了比密集型 27b 模型更快的性能。Qwen3.8-Next 模型在运行时采用 3 位量化版本,展示了 150 tps 的预填充和 3.6 tps 的解码。此性能超过了在同一 M5 硬件上 4 位密集型 27b 模型实现的 70 tps 预填充和 3 tps 解码。 AI

影响 展示了开源模型在消费级硬件上性能的提升,可能降低本地部署 LLM 的门槛。

排序理由 用户在消费级硬件上对开源模型的基准测试。

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-Next 模型在 M5 硬件上实现更快性能

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
Tool
用户在消费级硬件上对开源模型的基准测试。
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
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]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/maddie-lovelace ·

    Qwen3.8-Next 流式传输 - M5 Air 上 150tps 预填充,3.6 tps 解码

    <!-- SC_OFF --><div class="md"><p>Out of curiosity, I thought I'd see if I could adapt my <a href="https://www.reddit.com/r/LocalLLaMA/comments/1vjm6dn/300b_on_32gb_moestreaming_findings_optimisations/">DSv4 streaming stack</a> from a few weeks ago to take Qwen3.8-Next. It worked…