Google’s Algorithm “Nuclear Explosion” in Storage Chips! A New Battleground for the “Efficiency Revolution” in China’s AI Chip Industry

In March 2026, a technical paper from Google Research dropped a “deep-water bomb” on the global tech community. The AI memory-compression algorithm, dubbed TurboQuant, claims to reduce the critical memory footprint during large-model inference to one-sixth of its original size, while boosting performance by as much as eightfold.

Upon the release of the news, capital markets were immediately shaken. Shares of storage giants such as Micron, SanDisk, and Western Digital all plunged, with their combined market capitalization shrinking by more than US$90 billion in a single day. The A-share storage-chip sector was likewise hard hit, with stocks like GigaDevice and Biwin Storage posting significant declines.

Yet amid this industry-wide seismic shift driven by algorithms, a deeper question has come to the fore: as software efficiency leaps forward, has the fundamental logic underpinning hardware demand truly been upended? For domestic AI chip companies striving to catch up, is this a daunting challenge—or an unprecedented opportunity?

01. The “Nuclear Bomb” of Technology: How Does TurboQuant Revolutionize Memory Requirements?

To understand this shock, we must first grasp what TurboQuant has hit.

When large models process long texts, they must store historical dialogue information to form a “key-value cache” (KV Cache). This “short-term memory” expands linearly as the length of the conversation grows, making it a critical bottleneck that constrains AI inference costs.

Traditional quantization schemes often require a trade-off between compression accuracy and additional storage overhead. TurboQuant achieves a breakthrough through two innovations:

Step One : Geometric transformation. By employing the PolarQuant method, data are transformed from conventional Cartesian coordinates to polar coordinates, completely eliminating the additional memory overhead inherent in traditional approaches.

Step 2 : Error correction. By employing the one-bit QJL algorithm as a mathematical “error-correction engine,” it precisely eliminates the minute deviations introduced by compression, ensuring zero loss of model output accuracy.

The final result is : KV cache can be compressed to 3 bits without any retraining or fine-tuning, reducing memory usage by 83% while maintaining performance on par with the uncompressed model in benchmarks on open-source models such as Gemma and Mistral.

Cloudflare CEO Matthew Prince has dubbed this moment Google’s “DeepSeek moment.” Just as market concerns at the time of DeepSeek’s launch centered on a potential decline in computing power demand, the subsequent explosion of AI applications ultimately drove up hardware demand.

The release of the algorithm immediately triggered a knee-jerk sell-off in the capital markets.

On March 25, U.S. stocks opened with a sharp plunge in the memory-chip sector. SanDisk’s share price plunged as much as 6.5%, Micron Technology fell 3.4%, and Western Digital, Seagate Technology, and other major players followed suit. The negative sentiment quickly spread to Asian markets, with SK Hynix’s shares dropping 6.23% the next day and Samsung Electronics declining 4.71%.

The more direct impact is evident in the consumer market. Prices for DDR5 memory on U.S. retail platforms have fallen sharply, with some models seeing declines of over 20%. Industry insiders interpret this as a short-term inventory-clearing move by manufacturers following the release of new pricing algorithms.

However, a level-headed voice from Wall Street soon emerged. In its latest research report, Morgan Stanley explicitly stated that the market has gravely misinterpreted the situation.

This technique applies solely to the key-value cache during the inference phase, leaving the high-bandwidth memory (HBM) used for model weights unaffected and having no impact on AI training tasks. Moreover, the so-called “sixfold compression” does not reduce overall storage requirements; rather, it boosts single-GPU throughput by improving efficiency.

More critically, the “Jevons Paradox” was put forward: technological progress enhances efficiency, yet resource consumption not only fails to decline—it may even surge. For instance, Watt’s improvements to the steam engine increased coal combustion efficiency, yet this ultimately led to a dramatic spike in global coal demand.

When the memory threshold drops to one-sixth of its original level, complex AI applications that were once stifled in their infancy by prohibitive hardware costs will experience an explosive surge in adoption. This newly unleashed incremental demand will not only offset the space squeezed out by technological compression but may even set new demand peaks.

The emergence of the TurboQuant algorithm marks a new phase in the AI industry’s competitive landscape—shifting the focus from a mere “computing-power arms race” to an “efficiency revolution.” For domestic AI chip companies, this signifies a fundamental transformation in the dimensions of competition.

Opportunity: The Reasoning Market and Ecosystem Reconstruction

1. The Golden Window for the Reasoning Market TurboQuant primarily optimizes the inference phase of large models. By 2026, as large models are increasingly deployed across industries, the AI inference market is poised for explosive growth. Unlike training tasks, which prioritize raw computational power, inference places greater emphasis on cost efficiency, energy efficiency, and practical applicability—creating room for domestic chip companies to differentiate themselves through competitive advantages.

2. An Opportunity to Break Ecological Dependence : Inference workloads exhibit relatively low dependency on the traditional GPU ecosystem, such as CUDA. This presents a valuable opportunity for domestic chip manufacturers adopting proprietary or heterogeneous architectures, reducing their reliance on a single technological path and increasing the potential for “changing lanes to overtake.”

3. Catalyzing Growth in Edge and End-User Markets : The substantial increase in efficiency has significantly lowered the barrier to running large models. This will greatly accelerate the penetration of AI capabilities into edge devices, mobile terminals, and IoT devices, giving rise to an incremental market that is even larger than the cloud-based market and opening up vast opportunities for domestic companies focused on specialized inference chips and energy-efficiency optimization.

Challenge: A Leap in Core Competencies

The real challenge lies in the shift of competitive focus from hardware specifications to “integrated software–hardware system-level capabilities.” Whoever can more deeply fuse algorithmic innovation with chip design to deliver higher “effective computing power” and better “energy efficiency” will emerge victorious in this new phase.

Efficiency is emerging as the new “moat” in the AI chip industry. TurboQuant is just the beginning—it signals that, in the future, what will truly determine the competitiveness of AI chips is not merely peak computing performance, but rather the ability to seamlessly integrate with upper-layer algorithms and frameworks to deliver optimal total cost of ownership in real-world applications.

For the domestic chip industry, this requires companies to:

• Strengthen co-design capabilities for algorithms and chips, providing architectural-level support for emerging techniques such as model compression and sparsification.

• Build a more comprehensive software stack and toolchain to lower the barrier to entry for developers and enhance the efficiency of delivering “effective computing power.”

• We deeply cultivate vertical applications, delivering irreplaceable solutions in fields such as intelligent driving, smart manufacturing, and robotics—sectors that demand stringent real-time performance and energy efficiency.

The turbulence sparked by TurboQuant is, at its core, a microcosm of how technological advancement drives industrial evolution. It will not put an end to AI’s insatiable demand for computing power, but it will reshape the rules of competition.

While the domestic AI chip industry is bound to experience short-term growing pains, in the long run, a competitive landscape that prioritizes efficiency, embraces openness and diversity, and is closely aligned with real-world applications will serve as fertile ground for upending the status quo and nurturing new industry leaders. With efficiency emerging as the new currency, the true race is only just beginning.