AI Memory Compression Technology Rocks Semiconductor Market: How TurboQuant Shakes the Trillion-Dollar Hardware Landscape​

On the local time of March 26th, the release of TurboQuant, an AI large model memory compression technology developed by Google DeepMind's European team, sent shockwaves across the global technology industry. This technological breakthrough, which claims to reduce the memory footprint of large model Key-Value Cache (KV Cache) by 6x and boost inference performance by 8x without any loss of precision, directly triggered a collective plunge in storage chip stocks across Europe, the United States, and Asia — leading companies such as Micron, SanDisk, and SK Hynix saw intraday declines exceeding 5%, with the global storage industry's market value evaporating over 60 billion US dollars in a single day. An algorithmic revolution born in a German laboratory is rapidly rewriting the supply and demand logic of the AI computing power and semiconductor industries.​

The disruptive nature of TurboQuant precisely targets the core pain point of AI large model operations: the "memory wall." When current mainstream Transformer-based large models perform inference, they need to continuously cache processed contextual data (KV Cache). As demand for long texts and multi-turn conversations surges, the memory occupied by KV Cache expands exponentially, even exceeding the model's own weights, becoming the biggest bottleneck restricting AI deployment efficiency. To support long-context inference, cloud service providers and enterprises have to purchase massive amounts of High-Bandwidth Memory (HBM) and GDDR video memory. The memory cost of AI servers now accounts for over 40% of the total, with a single device's memory investment often reaching hundreds of thousands of yuan. Traditional quantization compression technologies generally suffer from precision loss and require additional storage for calibration parameters, with actual compression efficiency only ranging from 30% to 50%, making it difficult to address this dilemma.​

Dual technological innovations by DeepMind's European team enabled TurboQuant to achieve an industry breakthrough. Firstly, it adopts the self-developed PolarQuant polar coordinate quantization technology, converting high-dimensional vectors into polar coordinate form. Leveraging the concentrated distribution of angular parameters, it completely eliminates the normalization constants necessary for traditional quantization, thus removing additional storage overhead. Secondly, it utilizes the Quantized Johnson-Lindenstrauss (QJL) transform to complete compression error correction with only 1 bit of sign bit, achieving near-lossless precision guarantee. Ultimately, this technology compresses 16bit-32bit KV Cache to 3bit, and it can be plug-and-play for all Transformer-based large models without the need for retraining, resulting in an extremely low threshold for implementation.​

The drastic reaction in the capital market essentially stems from panic over the restructuring of AI storage demand logic. As the core driver of the current semiconductor recovery, AI computing power demand has fueled a skyrocketing growth in sales of HBM and high-end DRAM. The global AI server memory market size exceeded 80 billion US dollars in 2025, with an annual growth rate of over 150%. Storage chip manufacturers have fully bet their future growth on the sustained explosion of AI memory demand, with stock prices generally rising by more than 50% year-to-date and valuations at historical highs. The emergence of TurboQuant directly undermines this core logic: if the memory demand per server decreases by 6x, AI memory procurement volume will be significantly reduced, and the growth expectations of the storage industry will collapse instantly — this is the key reason for the collective decline in leading enterprises' stock prices.​

However, a rational analysis reveals that the market panic is an overreaction, and the impact of TurboQuant needs to be viewed in phases. In the short term, the technological impact is concentrated on the inference link, with no impact on the memory demand for AI model training. Currently, the AI industry is still in a period of rapid model iteration, and the rigid demand for HBM during the training phase remains strong. Global AI server shipments are expected to grow by 180% in 2026, and the HBM capacity gap has not yet been filled. Meanwhile, TurboQuant only compresses KV Cache and does not involve the storage of model weights, which account for over 60% of the total memory, so the core demand for high-end memory remains intact. In the medium term, the implementation of this technology will lower the threshold for AI deployment, allowing small and medium-sized enterprises and edge devices to access large models. The number of concurrent AI requests and the scale of applications will grow exponentially, ultimately driving total storage demand to rise rather than fall. In the long run, the industry will shift from "hardware stacking for capacity expansion" to "software-hardware collaborative optimization," and storage manufacturers need to transform from mere memory suppliers to providers of AI algorithm-adapted customized product development.​

The profound significance of this technological shock lies in marking a major shift in the development logic of the AI industry. Over the past five years, the improvement of AI performance has been highly dependent on hardware iteration — from A100 to H100 and then to H200, computing power growth has mainly relied on chip process upgrades and memory expansion. The emergence of TurboQuant proves that there is still enormous potential for algorithm optimization, and through extreme innovation at the software level, efficiency improvements far exceeding those of hardware upgrades can be achieved. This trend will reshape the global technological competition pattern: Europe seizes the high ground in AI efficiency optimization through algorithmic innovation, American hardware manufacturers face pressure to adjust their demand structure, and the Asian semiconductor industry needs to accelerate technological transformation. From the technological breakthrough in a German laboratory to the drastic shock in global capital markets, the TurboQuant incident reveals the core law of the technology industry: the boundary between algorithms and hardware is rapidly fading, and innovation has no boundaries. The short-term market panic will eventually subside, but the industrial transformation brought about by the technological revolution has just begun. The future competition in the AI industry will no longer be a mere hardware arms race, but an all-round contest of algorithmic innovation, hardware architecture, and scenario implementation. Storage chip manufacturers need to accelerate their transformation from "capacity suppliers" to "AI computing power solution providers," while AI enterprises must regard algorithm optimization as their core competitiveness. As TurboQuant gradually moves towards commercialization, the integrated innovation of the AI and semiconductor industries will usher in a new wave of technological revolution and industrial transformation.​