PulseAugur
中
实时 01:28:30

DeepSeek V4.1 Flash 1017 EXL3 2.9bpw 在 APEX 2025 上进行基准测试

一位用户对 DeepSeek V4.1 Flash 1017 EXL3 2.9bpw 模型进行了基准测试,发现其性能与六个月前的最先进模型相当。使用 APEX 2025 数据集作为基准,用户在配备六个 3090 GPU 的系统上实现了约 1000 tokens/秒的预填充速度和 75 tokens/秒的解码速度。用户分享这些结果是为了帮助他人对自己的设置进行基准测试。 AI

影响 为特定的模型量化提供了性能见解,帮助用户评估其能力。

排序理由 特定模型变体的用户生成基准测试结果。[lever_c_demoted from research: ic=1 ai=1.0]

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 →

DeepSeek V4.1 Flash 1017 EXL3 2.9bpw 在 APEX 2025 上进行基准测试

本文如何被排名

Signal score
18 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
特定模型变体的用户生成基准测试结果。[lever_c_demoted from research: ic=1 ai=1.0]
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
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 Nederlands(NL) · /u/hp1337 ·

    DeepSeek V4.1 Flash 1017 exl3 2.9bpw APEX 2025 基准测试结果

    <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1xtyxl9/deepseek_v41_flash_1017_exl3_29bpw_apex_2025/"> <img alt="DeepSeek V4.1 Flash 1017 exl3 2.9bpw APEX 2025 benchmark results" src="https://preview.redd.it/9ncvc7y7w21i1.jpeg?width=640&amp;crop=smart&amp;…