Does Google's New TurboQuant Technology Mean the Party's Over for Micron?

A little over a year ago, A Chinese quantitative hedge fund-turned-AI lab released an advanced AI model called DeepSeek. While there is some debate about exactly how cheaply and on what chips DeepSeek was trained, there is no doubt that DeepSeek implemented novel innovations that greatly boosted the efficiency of training an AI model with fewer and "less good" semiconductors.

AI semiconductor and memory stocks sold off sharply on the news, based on the surface-level impression that AI companies wouldn't need to buy so many logic and memory chips. However, we all now know that these stocks subsequently rebounded, and then some, as greater model efficiency didn't impede chip demand. Rather, AI companies used the efficiency gains to invest in even more advanced models, increasing overall demand for computing power and memory.

Last week, Alphabet's (NASDAQ: GOOG) (NASDAQ: GOOGL) Google Research released TurboQuant, a software-based AI memory compression technology that enables much more efficient inference with less memory. In response, major memory companies such as Micron (NASDAQ: MU) and its suppliers sold off sharply.

However, is this just another DeepSeek moment investors should buy?

What is TurboQuant?

TurboQuant significantly increases capacity and speeds up key-value cache (KV cache) in AI inference. KV-cache is a type of memory that enables an AI algorithm to retain previous context without recalculating all prior tokens to generate new ones. KV-cache is, therefore, a sort of "story" of the AI's prior output.

But if KV-cache is the "story" of past context, TurboQuant is a quick but accurate "summary" of that story.

In layperson's terms, TurboQuant works like this. An AI model understands context by storing data as vectors, or multidimensional charts with several "embeddings," or points within an X-Y-Z axis. A token with a similar vector to another means it has a similar relationship.

For simplicity's sake, let's assume an X-Y plane. So one embedding might be delineated by the direction "go three spaces east and four spaces north."

TurboQuant simplifies these commands by saying, "go five spaces at 37 degrees northeast." This greatly reduces the computations needed to understand context, though it can lead to residual errors. But TurboQuant then overlays a 1-bit error-correction mechanism that cleans this up. Even with the extra bit, this technique uses much less memory than the standard XYZ coordinate method for AI vectors.

As a result of the error correction, Google Research claims TurboQuant can increase the capacity of the KV-cache by six times, while also making AI inference eight times faster -- all without any loss of accuracy.

TurboQuant turbo-charges AI inference. Image source: Getty Images.

How TurboQuant will affect AI memory

If AI inference can use six times less DRAM and run eight times faster, the thinking is that there may be less demand for memory in future inference applications.

This seems a bit simplistic, although there is a plausible downside case. One risk is that AI inference market share could shift from expensive GPUs with high-bandwidth memory (HBM) to CPUs running on "traditional" server memory such as DDR5 or MRDIMM.

HBM is much faster than these older types of memory, but it can hold less context and is much more expensive. Because of TurboQuant's eightfold increase in KV cache speed, a company now wanting to use many AI agents inferring on a large amount of data, such as a 1,000-page legal document, can perhaps deploy DDR5 or MR-DIMM more effectively. While HBM will also be supercharged by TurboQuant, older forms of memory used by CPUs could be "fast enough" for large enterprises looking to lower costs.

HBM has been one of the main factors in today's memory supply crunch, as it can take three to four times the equipment to produce a bit of HBM relative to "traditional" memory. So it's possible that as demand shifts to more traditional memory for inference, the memory market won't be so supply constrained.

But the bullish story is more likely

While TurboQuant poses a potential risk to the HBM market, which has been sucking up most of the industry's supply, this investor still thinks a bullish scenario is more likely.

First, HBM will also see improvements from TurboQuant, by allowing HBM-based inference to have larger context windows. So AI inference won't move entirely to CPUs or traditional memory. For applications that demand lightning-quick latency, HBM will still likely be deployed to some extent.

Also, HBM is still the main type of memory for AI model training, and TurboQuant doesn't affect that. While inference will be the bigger market in the future, HBM demand for training will still likely continue to increase. Given that we are currently very undersupplied in HBM, and that TurboQuant hasn't even been implemented outside Google's lab yet, memory companies will have time to adjust their supply growth accordingly.

But supply adjustments may not even be needed, as Jevon's paradox may hold for TurboQuant just as it held for DeepSeek. Jevon's Paradox states that when a process becomes more efficient, rather than using less of its inputs, demand actually increases for those resources, as greater efficiency unlocks adoption and more use cases.

Given that most top technology companies believe we are still at the beginning of the AI era, if TurboQuant speeds up the rate at which enterprises and consumers incorporate AI into their businesses, that rising tide of demand should lift all boats.

In sum, this memory sell-off could be an opportunity. While Micron and related semiconductor capital equipment stocks are still up tremendously over the past year, this "scare" could be a chance to add or buy an initial stake, if you missed out on the past year's run.

Billy Duberstein and/or his clients have positions in Alphabet and Micron Technology and has the following options: short January 2027 $110 puts on Micron Technology, short January 2027 $195 calls on Micron Technology, and short March 2027 $100 puts on Micron Technology. The Motley Fool has positions in and recommends Alphabet and Micron Technology. The Motley Fool has a disclosure policy.