Google made waves again, and this time the epicenter was the memory chip market.
Last Tuesday, the tech giant unveiled TurboQuant, a new compression technique for AI models that promises to reduce the amount of memory needed to run large language models by up to 6 times. The announcement was published directly on the companys research blog and describes the approach as a way to redefine AI efficiency through extreme compression.
The announcement was enough to shake stock markets around the world.
Shares of memory chip manufacturers plummeted across different markets, from Seoul to Tokyo to Wall Street, raising a question the industry already knows well: will the world need less hardware to run AI?
The scene was very reminiscent of what happened with DeepSeek in early 2025, when the Chinese startup spooked the market with efficient and cheap models, tanking tech stocks in a single day. Not coincidentally, Matthew Prince, CEO of Cloudflare, labeled TurboQuant as Googles DeepSeek, pointing out that theres still plenty of room to optimize AI inference in terms of speed, memory consumption, energy costs, and multi-tenant utilization.
But before jumping to conclusions, its worth understanding what TurboQuant actually does, what experts are saying, and why greater efficiency doesnt always mean fewer chips. 👇
What is TurboQuant and how does it work
TurboQuant is a quantization technique developed by Google focused on reducing memory consumption during inference of large language models, the now-famous LLMs. In practical terms, quantization is the process of representing a models weights and intermediate data with fewer bits than the original format, which reduces the need for storage and memory bandwidth when running the model.
What Google did with TurboQuant was take this concept to a whole new level. According to the companys research paper, the technique focuses specifically on compressing the key-value cache, or KV cache, which is the structure responsible for storing the models previous calculations so it doesnt have to redo them with every new interaction. This cache is one of the biggest memory bottlenecks during inference, especially when models handle long contexts like extended conversations or large documents.
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By applying extreme compression to the KV cache, TurboQuant manages to free up a significant amount of memory without meaningfully compromising the quality of the models generated responses. What sets it apart from other existing quantization approaches is how the technique analyzes the relative importance of each layer and intelligently applies different levels of precision, preserving performance where it matters most and compressing aggressively where theres room for it.
To put it in perspective: a language model that previously required, say, 80 GB of memory to run could, with TurboQuant, operate on a fraction of that. This completely changes the hardware equation for production inference, especially for companies that need to scale AI usage without blowing up their infrastructure budget. And thats exactly the prospect that made markets react so quickly to the announcement. 📉
The immediate impact on memory chip stocks
The financial markets reaction was swift and intense. On the Thursday following the announcement, SK Hynix shares dropped 6% on the Seoul exchange, while Samsung fell nearly 5% on the same market. In Japan, flash memory maker Kioxia also posted a decline of nearly 6%. In the United States, Sandisk and Micron had already begun their downward move on Wednesday, and both continued falling in pre-market trading on Thursday.
Investors interpreted TurboQuant as a signal that future demand for high-bandwidth memory chips, known as HBM, could be lower than the market had been pricing in. After all, if an AI model needs less memory to operate, the immediate logic is that companies will buy fewer chips. That reasoning has a real basis, but as well see shortly, it ignores important historical factors in the tech sector.
The stock decline was even more striking when you consider the context. The three largest memory manufacturers in the world had been riding an extraordinary rally over the previous twelve months. Samsung shares had climbed nearly 200% over the past year, fueled by AI demand. Meanwhile, Micron and SK Hynix had racked up gains exceeding 300%. With such aggressive valuations, any negative news could serve as a trigger for profit-taking, and thats exactly what happened.
Profit-taking or genuine panic
Industry analysts were quick to put the move into context. Ben Barringer, head of technology research at Quilter Cheviot, explained that memory stocks had been on a very strong run and that the sector is highly cyclical, meaning investors were already looking for reasons to take profits. According to him, TurboQuant added pressure to the picture, but its something evolutionary, not revolutionary, and doesnt change the long-term demand outlook for the industry.
In other words, the market was already primed for a correction. TurboQuant served as the catalyst, but not necessarily as the fundamental cause of the drop. In an environment where stock prices already reflected extremely optimistic expectations about the future of memory demand, even an incremental development can be used as a reason to lighten positions.
This pattern is nothing new in the tech world. The same thing happened with DeepSeek in early 2025, when the revelation that competitive models could be trained on much smaller budgets triggered a massive sell-off in Nasdaq stocks. At the time, the actual impact on chip demand turned out to be far smaller than the market initially feared, and stocks recovered in the following weeks. The question now is whether history will repeat itself with TurboQuant. 🤔
Why AI efficiency isnt the end of memory chips
AI and tech infrastructure experts have been pretty clear on one point: greater efficiency doesnt eliminate the need for memory chips — it transforms that need. Ray Wang, a memory analyst at SemiAnalysis, was blunt in saying that Googles research wont necessarily lead to a need for fewer chips. According to Wang, the KV cache is a critical bottleneck that needs to be solved for models and hardware to perform better, and solving that bottleneck makes AI hardware more capable, not less necessary.
Wangs logic follows what economists call the Jevons Paradox. This concept, formulated in the 19th century, states that when a resource becomes more efficient, total consumption of that resource tends to increase, not decrease, because greater efficiency makes the resource more accessible and more widely used. In the context of chips and AI, this means that if running large models becomes cheaper, more companies will run more models, more often, across more applications — which could maintain or even increase hardware demand over the long run.
Wang reinforced this point by explaining that it will be hard to avoid greater memory usage as model performance improves. When a bottleneck is removed, hardware becomes more capable, training models grow more powerful, and more powerful models require better hardware to support them. Its a feedback loop that has historically always driven demand for computing components — not the other way around.
Inference versus training: an important distinction
A technical detail that many investors may have overlooked when reacting to the announcement is that TurboQuant was developed with a focus on inference — that is, the phase where the model is already trained and being used to generate responses. The training of new AI models, which is where the bulk of hardware consumption happens, is not directly affected by quantization techniques like this one.
Google, OpenAI, Anthropic, and other major AI companies continue investing billions of dollars in training infrastructure, and the race for increasingly capable models shows no signs of slowing down. In fact, Demis Hassabis, CEO of Google DeepMind, had already publicly signaled that research and deployment of agentic AI are being held back precisely by the scarcity of available memory chips on the market.
What changes with TurboQuant is where and how these models are deployed after training, not the pace at which theyre developed. In practice, this could even increase pressure on the memory supply chain, since models that are more efficient at inference tend to be adopted by a larger number of companies and across a wider range of use cases.
The ripple effect of democratizing AI
When language models become lighter and cheaper to run, the natural tendency is for AI usage to expand into new use cases that were previously financially impractical. Smaller companies gain access to technologies that were once exclusive to large corporations. Real-time applications, edge computing, and mobile devices gain the ability to run sophisticated models. And all of this, in aggregate, represents more demand for processing and memory, not less.
Think of it this way: if previously only the ten largest tech companies in the world had the budget to run massive language models in production, and now a thousand companies can do the same thanks to TurboQuants efficiency, total memory chip consumption could very well increase, even if each individual company needs less hardware. Its the scale that changes the equation.
On top of that, TurboQuant could actually accelerate the adoption of larger and more complex models by companies that already have robust infrastructure. If an organization previously needed all of its memory capacity to run a cutting-edge model, that same organization can now use the freed-up memory to run even larger models or to process more simultaneous requests. The capacity ceiling goes up for everyone. 🚀
The market context that cant be ignored
Despite the stock drop last week, a combination of factors continues to support the memory market over the long term. Significant demand for high-bandwidth chips, combined with supply that still cant keep up with consumption, has been pushing memory prices to unprecedented levels and sustaining profits for Samsung, SK Hynix, and Micron.
Microns own CEO, Sanjay Mehrotra, has publicly stated that memory chip supply is tight and that the company cant deliver enough to meet customer demand. As long as this imbalance between supply and demand persists, its hard to argue that a compression technique, no matter how impressive, will structurally undermine the markets need for these components.
The reality is that the semiconductor industry operates in cycles, and the current cycle is still strongly favorable for memory manufacturers. Data center investments continue to grow, governments are subsidizing the construction of new chip fabs, and the AI race is far from peaking. TurboQuant may shift the composition of demand, but it will hardly reverse the growth trend.
What we can take away from all of this
The stock market moves following the TurboQuant announcement are a reminder of how financial markets are still learning to interpret innovation cycles in artificial intelligence. With every efficiency breakthrough, theres a panic reaction about the future of hardware demand, and every quarter, chip consumption growth numbers keep surprising to the upside. That doesnt mean the market is wrong to pay attention to these innovations, but it does mean the analysis needs to go beyond the immediate impact and consider the long-term systemic effects.
With TurboQuant, Google is essentially democratizing access to powerful language models. Reducing the memory needed to run large LLMs by up to 6 times is an advancement that benefits everyone from startups to everyday users interacting with AI-powered products. The technology becomes faster, cheaper, and more accessible — and historically, that has never been bad news for the tech sector as a whole, even if it creates short-term turbulence for specific market segments.
At the end of the day, TurboQuant is just another chapter in a story we already know well: artificial intelligence is getting more efficient, and that efficiency is opening doors to new uses, new products, and new demands. Memory chips arent going anywhere, but the type of chip, how theyre used, and who has access to them could change quite a bit in the coming years. And keeping a close eye on this shift is essential for understanding where technology is headed. 👀
