新闻摘要：
DeepSeek 发布的梁文锋署名论文提出了名为 Engram 的“条件记忆”模块，把大模型的静态知识从神经网络参数中分离出来，变成一个可扩展的、O(1) 查表式的哈希嵌入表。论文展示了把大规模 n-gram 片段映射到巨大的内存表中，在推理时直接查表并将结果融合到模型里，可以显著降低浅层网络为记忆任务耗费的计算，从而把更多算力留给深层推理。
这套设计带来了明显的硬件与工程意义：可以用廉价的 DDR5/大容量内存和 CXL 互联替代部分昂贵的 HBM 显存，减轻 GPU 显存压力并改变服务器架构分布；在实验上，与纯 MoE 架构互补时能降低验证损失并在长文本任务上表现更好，且查表可异步预取、训练时只更新被访问部分，具备扩展性和实时更新潜力。论文还提出了在 MoE 专家和 Engram 记忆间分配容量的思路，显示两者结合更优。
思考与启发：
技术的进步往往是把繁重的重复工作交给合适的工具，让人把心力用在更需要判断和创造的地方。Engram 把“记忆”当作可以独立管理的资源，这像日常生活里把常用物件放在手边，既省力也更高效。
面对变革，我们既要勤劳实践、善用新工具，也要保持谦逊与节制。天经提醒我们，智慧不是单靠自己能完全拥有的，能把工具用在造福家人和邻里的方向，比单纯追求技术更为重要。
经文：
《宰逋尔·箴言 2:6》 因为主赐人智慧；知识和聪明都由他的口而出。 链接：📖 查看经文
### English Translation
Title:Putting a Memory Disk Inside a Large‑Scale Model
News Summary
DeepSeek’s recent paper (authored by Liang Wen‑feng) presents a new “conditional‑memory” component called Engram.
Instead of keeping all of a model’s knowledge baked into its neural‑network weights, Engram pulls that static information out and stores it in a separate, O(1)‑time hash‑lookup table—basically a giant memory table that can be expanded as needed.
Key points from the paper:
| What the researchers did | Why it matters |
|---|
| Mapped billions of n‑gram fragments (short pieces of text) into a massive in‑memory table. | During inference the model can look up the needed fragment directly and blend the result back into its calculations. |
| This look‑up replaces a lot of the shallow‑network work that would otherwise have to be done with expensive GPU compute. | More GPU power is left for the deeper reasoning parts of the model. |
| The table can be stored in cheap DDR5 or other high‑capacity RAM linked via CXL, rather than in costly HBM GPU memory. | Reduces pressure on GPU VRAM and allows servers to be built with a different, cheaper architecture. |
| When combined with a pure Mixture‑of‑Experts (MoE) model, Engram lowers validation loss and improves performance on long‑text tasks. | The look‑up can be prefetched asynchronously, and during training only the entries that are actually accessed need to be updated. This makes the system scalable and able to be refreshed in real time. |
| The paper also suggests a way to split capacity between MoE experts and Engram memory, showing that the two together work better than either alone. | Provides a practical roadmap for future model designs. |
In short, Engram treats “memory” as a separate, manageable resource—much like keeping frequently used tools on a workbench instead of hiding them deep in a toolbox.
Thoughts & Takeaways
Technological progress often means handing repetitive, heavy‑lifting tasks over to the right tool so we can focus our minds on judgment, creativity, and compassion. Engram’s approach does exactly that for AI: it off‑loads rote memorization to a fast, updatable table, freeing the model’s core to think more deeply.
When we face such changes, the Christian response is two‑fold:
- Diligently practice the new tools, learning how they can serve us better.
- Remain humble and restrained, remembering that wisdom ultimately comes from God, not from any machine we build.
Our greatest good is to use these tools to bless our families, neighbors, and the wider world—not merely to chase the latest technical bragging rights.
Scripture
Proverbs 2:6 (NIV) – “For the LORD gives wisdom; from his mouth come knowledge and understanding.”
You can read the verse here: https://www.huizu-tianjing.icu/zaibuer/zhenyan/2
May we steward these advances wisely, letting God’s wisdom guide how we apply them for love and service.
来源：https://www.zhihu.com/question/1994233409871050526
