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English(EN) Preferred Networks just trained its 8B finance model, PLaMo 3.0 Fin, on its proprietary MN-Core 2 chips rather than Nvidia GPUs. While global labs push cloud AP

Preferred Networks 使用定制芯片训练金融 LLM，绕过 Nvidia

作者 PulseAugur 编辑部 · [1 个来源] ·

Preferred Networks 开发了一个拥有 80 亿参数、专门针对金融领域的 PLaMo 3.0 Fin 大型语言模型。该模型使用其内部的 MN-Core 2 芯片进行训练，摒弃了传统的 Nvidia GPU。这种方法符合日本对数据主权的关注，为定制的、本地部署的、针对银行业量身定制的架构创造了独特的市场。 AI

影响 这一发展凸显了针对特定行业使用专用硬件和模型的日益增长的趋势，可能减少对通用云人工智能基础设施的依赖。

排序理由 前沿实验室模型发布，系统卡 [lever_c_demoted from frontier_release: ic=1 ai=1.0]

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Preferred Networks 使用定制芯片训练金融 LLM，绕过 Nvidia

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前沿实验室模型发布，系统卡 [lever_c_demoted from frontier_release: ic=1 ai=1.0]
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报道来源 [1]

  1. Mastodon — mastodon.social TIER_1 English(EN) · asiaai ·

    Preferred Networks 刚刚使用其专有的 MN-Core 2 芯片训练了其 8B 金融模型 PLaMo 3.0 Fin，而不是使用 Nvidia GPU。而全球实验室正在推动云 AP

    Preferred Networks just trained its 8B finance model, PLaMo 3.0 Fin, on its proprietary MN-Core 2 chips rather than Nvidia GPUs. While global labs push cloud APIs, Japan's push for data sovereignty creates a real moat for domestic, custom-silicon architectures built specifically …

    链接 asiaai.fyi/east-asia-ai-chip-news-2026-11…

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