A research report from GLMS SEC states that Google's TurboQuant technology achieves lossless and efficient memory compression. The significant leap in memory efficiency brought by TurboQuant is expected to drive growth in storage demand over the long term through multiple transmission logics, rather than compressing the market space. Memory compression technology may encourage developers to utilize the saved memory space to expand larger context windows, supporting scenarios such as million-token full codebase analysis and ultra-long video understanding. This new demand is likely to consume the freed-up memory space. Domestic module manufacturers primarily focus on NAND-based products, where long-term demand remains relatively stable, and the upward price trend is expected to continue. The main views of GLMS SEC are as follows:

Google's TurboQuant technology achieves lossless and efficient memory compression. The TurboQuant memory compression technology introduced by Google enables significant model size compression with almost no loss of precision, adapting to key scenarios like key-value cache compression and vector search. In LongBench benchmark tests on the Llama-3.1-8B-Instruct model, the technology's 3.5-bit quantization performance matched that of 16-bit full cache precision, with overall performance surpassing similar compression solutions. On H100 GPUs, 4-bit TurboQuant delivered an 8x performance improvement over unquantized 32-bit keys, with almost negligible runtime overhead.

Breakthroughs in memory compression technology will drive long-term expansion in storage demand. The leap in memory efficiency from TurboQuant is expected to stimulate storage demand growth over the long run through multiple transmission mechanisms, rather than shrinking the market. According to the "Jevons Paradox" in economics, this technology significantly lowers the computational and memory barriers for AI inference, potentially accelerating the penetration of AI capabilities across all scenarios. This could lead to a multiplicative increase in global AI server deployments, thereby boosting overall storage demand. Additionally, memory compression technology may prompt developers to use the compressed memory space to expand larger context windows, enabling applications like full codebase analysis with millions of tokens and ultra-long video understanding. This new demand is likely to consume the memory space that has been freed up.

NAND flash is on an upward pricing trajectory, with long-term demand supported by solid fundamentals. In the current storage industry price cycle, the pace and magnitude of NAND flash price increases differ significantly from those of DRAM, and NAND remains in a price uptrend with ample room for further gains. Historically, NAND price movements tend to lag behind DRAM by approximately six months. The current DRAM price surge began mainly in the second quarter of 2025, while the NAND price increase started primarily in the fourth quarter of 2025. DRAM prices have seen substantial cumulative gains, with some mainstream models rising several-fold or even dozens of times from previous lows. NAND prices are also trending upward and remain in an ascending channel.

In terms of investment targets, for storage modules, it is recommended to focus on: DMY Technology (001309.SZ), BIWIN Storage (688525.SH), and Longsys (301308.SZ). For niche storage, it is advisable to monitor: GigaDevice (603986.SH), Puya Semiconductor (688766.SH), and Fabulous Semiconductor (688110.SH).

Risk warnings include the potential for demand realization to be slower than expected, the risk of supply expansion mismatching demand, and the possibility that technological advancements could reshape the beneficiary structure.