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怎么把模型跑得更快更省钱:推理加速、显存与成本、服务部署与基础设施选型。

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6月18日周四第 41–44 条
  1. Jim Fan80

    Jim Fan 介绍了 ENPIRE 在按下回车前必须完成的工程设计,包括两层安全机制、冻结奖励函数和系统遥测。安全上设置硬性运动学限制,机器人越界即任务失败并自动重置,同时用限扭力柔性夹爪让错误接触安全停住;奖励上先用少量成功与失败演示训练视觉分类器,再将其冻结为实时奖励函数。

    引用Jim Fan@DrJimFan

    Today, we enable AutoResearch in the physical world for the first time! Introducing ENPIRE: we give 8 Codex agents a fleet of robots, an allocation of GPUs, and generous token budget. We set them free with a simple goal: solve the task as quickly as possible, keep the robots busy but stay safe, don't waste precious compute. Make no mistake. Then humans step aside and our watch begins. The robot fleet starts to come alive: they learn to look for visual clues, reset the scene, practice novel skills, tinker with control stack, read papers online, debate, reflect, get stuck, and try again directly on the hardware. All we did is to give Codex an API to the world of atoms, and the rest is emergence. ENPIRE is able to solve high-precision tasks like tying zip-ties, organizing fine pins, and installing GPUs all by itself. We also discovered a new type of "physical scaling": 8 robots exploring in parallel improves significantly faster than fewer ones. A part of our NVIDIA GEAR lab now self-improves tirelessly over night. We just read the reports in the morning. /goal: we all take a holiday and Jensen wouldn't even notice ;) We will be open-sourcing everything, so you can host your self-running robot lab at home too! Deep dive in the thread:

    推荐理由:作者拆解了让 8 个 Codex 智能体自主运行机器人实验室前必须搭好的安全、奖励与遥测设计。

6月8日周一
6月3日周三
  1. 阶跃星辰模型发布60

    阶跃星辰发布 Step-3.7-Flash GGUF 量化版本

    阶跃星辰为 198B 参数稀疏 MoE 视觉语言模型 Step-3.7-Flash 发布 GGUF 量化版本,含 BF16 到 IQ3_XXS 共 7 个语言量化档位及 4 GB 的 F16 视觉投影器。

    推荐理由:原文给出各量化档位的体积与三款 128 GB 统一内存设备上的实测吞吐,可据此判断本地私有部署的可行边界。

5月27日周三