Google's new algorithm that dramatically compresses KV cache in a lossless fashion, dubbed TurboQuant, is all the rage these days in the AI sphere, where doomsday predictions of an imminent collapse in memory demand abound. Never mind the fact that the underlying paper was released all the way back in April 2025!
Even so, we postulate that the current doom-and-gloom in the market is eerily similar to the one that prevailed immediately after DeepSeek released its R1 model in early 2025, and that Jevons paradox will prevail.
Google's TurboQuant to supercharge Jevons paradox effect, sky-high demand for memory resources to persist for the foreseeable future
Before going further, let's first discuss what TurboQuant actually does. Consider a scenario: you are writing a story, but hampered by terrible short-term memory. Whenever you write a new word, you are compelled to read whatever you've written so far just to remember what has already been inked. Obviously, as the text length increases, so does this laborious process.
Key-Value or KV cache is similar to taking notes on a separate sheet so that you remain abreast of what has been written so far. This speeds up the entire process by orders of magnitude. Google's TurboQuant compresses this KV cache for a given AI model by up to 6x, thereby speeding up the underlying model by up to 8x. What's more, TurboQuant is able to do so with zero accuracy loss.
Now that we've discussed what TurboQuant actually does, let's go over all of the recent doom-and-gloom surrounding this breakthrough. Basically, investors in high-flying memory stocks now fear that this algorithm would dampen the oncoming demand for memory resources just as major players start to embark on capacity expansion.
What many people have failed to grasp is the fact that TurboQuant does not actually compress model weights, which often dwarf KV cache in large deployments. This means that the model size remains the same. The algorithm dramatically improves inference-related economics for data centers by allowing for an increase in a given model's context window (number of tokens) or by enabling a smaller number of GPUs to handle the same number of users.
Far from decreasing the demand for memory resources, this development actually invokes the Jevons paradox, which postulates that a technology's use increases as its operating cost decreases. Consequently, it would be facile to believe that the ongoing memory crunch will end anytime soon.
Finally, the interplay with Jevons paradox also means that we should not expect the ongoing upheaval in the consumer electronics sphere, especially the memory chipflation-driven price increases for smartphones, to moderate in the near future.
About the author: Writing is my one incontrovertible passion. Over the past six years, he has authored over 2,200 distinct articles on financial and tech-related topics, spanning nearly 1 million words. And he has been a member of Wcctech mobile team since 2025. As an alumnus of the University of Toronto, Rotman Commerce Program, I bring nuance, in-depth knowledge, and a unique perspective to every topic that I cover. When I'm not writing, I'm traveling the world, exploring hidden confectionaries and restaurants as an aspiring food connoisseur.
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