# DeepSeek：将硬件稀缺性转化为可编程的战略优势

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-05-24 13:39
- AIHOT 分数：62
- AIHOT 链接：https://aihot.virxact.com/items/cmpjd8jwo14nmsljw2uq2z0ep
- 原文链接：https://x.com/rohanpaul_ai/status/2058422499256439044

## AI 摘要

DeepSeek的核心战略并非开发廉价聊天机器人，而是通过一系列架构创新（如MoE动态激活、DSA优化、CSA/HCA技术）显著降低对高端HBM GPU的依赖。此举旨在将硬件稀缺性转化为技术优势，使次优芯片、LPDDR内存及定制ASIC能支持前沿AI，从而优化AI以适配不同的工业基础。这一路径已产生实际商业影响，如V4-Pro大幅降价并与国产硬件生态形成联动，最终目标是实现“硬件稀缺性可编程”。

## 正文

Great article here on DeepSeek.

Their real story is not cheaper chatbots， but architecture that turns hardware scarcity into strategy.

DeepSeek is not trying to sell coding seats， it is trying to make Chinese memory， accelerators， and systems useful for frontier AI.

Every recent DeepSeek move attacks a bottleneck that makes frontier models dependent on elite HBM-heavy GPU stacks： MoE activates only parts of a model， DSA reduces long-context attention cost， and V4-Pro's official card says CSA/HCA cuts 1M-token single-token inference FLOPs to 27% and KV cache to 10% of V3.2.

Engram， a separate research line， pushes the same logic from another side： let static knowledge live in scalable lookup memory， then fetch it predictably from host memory instead of forcing every fact through dense computation.

That sounds like engineering detail until you see the business consequence.

If models need less HBM and less brute-force compute， then second-best chips， abundant LPDDR， NAND， and customized ASICs become less second-best.

Reuters has already reported a permanent 75% DeepSeek V4-Pro price cut， while noting Huawei Ascend supply constraints and expected supernode availability， which is exactly the kind of feedback loop that they wanted.

DeepSeek is not only optimizing models for benchmarks， it is optimizing AI for a different industrial base.

The prize is not the app layer.

The prize is making scarcity programmable.

### 引用推文

> GDP：http://x.com/i/article/2057886253249662976