r/stockmarket
超大规模云厂商正实施可将内存使用量压缩高达40倍的技术
随着ASP见顶，超大规模云厂商采用NVDA的KVTC等新型内存压缩技术，威胁到内存股“周期已死”的叙事。
- DRAM/NAND现货价格似乎已经见顶，且随着中国内存厂商竞争加剧，价格可能会继续下跌。
- 历史上，内存股股价见顶通常比实际DRAM价格见顶早5到8个月。
- 超大规模云厂商正在开发如NVDA的KVTC等技术，可将内存使用量压缩20至40倍，从根本上挑战了‘周期已死’的叙事。
在美光（MU）发布了一份惊人的财报后，目前所有人都在纷纷涌入DRAM ETF、MU和SNDK。营收、盈利和利润率增长的主要驱动力，是随着AI基础设施建设推进，内存需求上升所导致的DRAM/NAND价格大幅上涨。因此，内存公司直接受益于超大规模厂商疯狂的资本开支。
新的叙事是，这个历来具有周期性的子行业将不再具备周期性，因为随着芯片和数据中心持续演进，对内存的需求将变得持续且稳定。
再次强调，美光财报中一个有趣之处在于，出货量增长并未显著推动营收增长（某些业务部门的出货量甚至下降了），真正推动业绩爆表的是ASP（平均售价）的急剧攀升。目前，DRAM/NAND闪存现货价格似乎已见顶，随着多家中国内存厂商不断进入市场，未来价格很可能会继续下行。此外，在过去的DRAM周期中，内存股票价格通常在实际DRAM价格见顶前5到8个月就已达到峰值。
但在“周期性已死”这一讨论中，很少有人提及大型内存买家正在积极研究并计划实施的措施——这些措施旨在优化未来内存使用效率。
附表展示了美国科技巨头开发的一些技术，可将内存使用量压缩20至40倍。
简要介绍英伟达（NVDA）的KV缓存变换编码（KVTC）方法：
KVTC是一种借鉴传统媒体压缩技术的方法，能大幅缩小大语言模型（LLMs）中键值（KV）缓存的体积。通过应用主成分分析（PCA）和熵编码，该方法可在不修改模型权重的前提下，将内存需求降低最多20倍（某些场景下可达40倍）。
HBM space saved by KVTC compression is going to be immediately consumed by hyperscalers deploying next-generation trillion-parameter LLM weight. This KVTC will fuel the market even more
This is what I thought. But your number is off - 1T params is not very big these days, Mythos/Fable is estimated to be 10T params model. GPT-4 from early 2023 was estimated to be 1.8T model.
I think we'll see more 10T+ models soon especially with the new memory improvements.
Jevons Paradox says fuck your puts
This message brought to you by SK Hynix (wink)
Right but when you bottleneck narrative disappears, your stock will no longer be elevated. The market currently only likes bottlenecks. They don't actually care how much you're growing or what your margin is. Otherwise SaaS and Mag 7 wouldn't have been sold off.
Compression you say? What’s the Weissman score with this KVTC method??
Their new middle-out technique will break the Weissman score.
5.1 it’s a new record!
Same articles came out last earnings. Did the same with google and Nvidia. Just manipulation of retail
Efficiency improvements often expand the market because they lower the cost of doing more work. That’s especially true for agentic AI.
Suppose Google cuts KV cache memory by 6× using TurboQuant. Instead of running one agent, they may now run six agents simultaneously, support much longer context windows, or serve many more users. The saved memory is often immediately reinvested into capability.
The chart also tells you where the industry believes the bottlenecks are. None of them eliminate the need for memory.
Like adding another lane to the highway. Traffic problem doesn't get solved, but it does enable people to live farther from work.
Wait till your port gets compressed by 95%
I'm in chips since 2011.
Been hearing cyclicality is dead every upcycle. Good for me, I get richer. But the downcycle always comes after.
A.L.W.A.Y.S
This RAM shortage in particular.... It's days are SO counted. China is ramping up production right now. And we all know what happens when the Chinese government decides to corner a market. They'll overproduce like crazy, and the price of RAM will go back to where it was before 2020, perhaps even less
This is what I never understood.
There is a practical case for A.I production and its disinflationary impact but it was always about a couple of key questions that one - trick pony bulls are violently opposed to:
- what is the economics like after the CapEx spending?
- if memory cyclicity no longer holds true, where will the additional liquidity, almost all of which are currently from bond offerings, free cash flow, debt market and private credit market, that funds these memory and data centers buildout come from?
- hyperscalers are used to dominating the market: theyay be forced to accept exorbitant prices now but behind closed doors they are strategising means to break memory's maker grip on the memory market.
Didn't Nvidia's scare from Deepseek, Google's TPU and AMD taught anyone anything?