Memory stocks plunge today as Google's TurboQuant triggers a 'DeepSeek moment'
Google's TurboQuant promises an 8x reduction in AI memory needs. Memory maker stocks have collapsed. Is it the end of the bubble or an overreaction?
English · Original discussion in Spanish · Published
Google's TurboQuant sinks memory stocks: paradigm shift or overreaction?
Google's new algorithm, TurboQuant, has triggered a historic plunge in the shares of memory makers (Micron, Samsung) by promising a reduction of up to 8x in the memory needed for AI inference, with cost savings of over 50%. The market reacted with massive selling, interpreting that future demand for memory chips will fall drastically. However, the technical analysis reveals crucial nuances.
What is TurboQuant and how does it affect memory?
TurboQuant focuses on quantizing the KV (Key-Value) cache during inference of large language models (LLMs). This cache is stored in the VRAM of GPUs or TPUs, not in system RAM. The optimization allows models to run with less dedicated memory, but it does not eliminate the need for conventional RAM nor does it affect model training. The stock market panic has mixed both concepts: demand for RAM for PCs and servers is not directly affected.
The debate: burst bubble or oversold?
One line of analysis holds that the memory bubble, inflated by speculative AI demand, has burst. They point out that the "picks and shovels" market of a gold rush always ends in a correction. Other experts argue that the drop is an overreaction: TurboQuant only optimizes inference, a smaller segment of total memory consumption, and training needs remain sky-high. Moreover, quantization is not a new technology; Google has simply applied it more efficiently.
The data doesn't lie: a 100-billion-parameter model at fp16 requires 200 GB just for the weights, not counting the cache. TurboQuant reduces cache memory, but not the weights. For personal computers, DDR5 RAM prices remain high (32 GB above 300 euros), and no immediate price drop is expected.
A buying opportunity?
Some analysts see the correction as a chance to buy memory stocks at a discount, arguing that structural semiconductor demand (AI, automotive, IoT) has not disappeared. Others, more skeptical, remind us that memory companies have historically been cyclical and that efficiency innovation always ends up reducing the volumes needed.
The open question is whether TurboQuant is an isolated breakthrough or the start of a trend that will make memory cease to be AI's bottleneck. For now, the market has spoken, but the technology keeps evolving.
Related discussions in the forum
Google's new algorithm, TurboQuant, has triggered a historic plunge in the shares of memory makers (Micron, Samsung) by promising a reduction of up to 8x in the memory needed for AI inference, with cost savings of over 50%. The market reacted with massive selling, interpreting that future demand for memory chips will fall drastically. However, the technical analysis reveals crucial nuances.
What is TurboQuant and how does it affect memory?
TurboQuant focuses on quantizing the KV (Key-Value) cache during inference of large language models (LLMs). This cache is stored in the VRAM of GPUs or TPUs, not in system RAM. The optimization allows models to run with less dedicated memory, but it does not eliminate the need for conventional RAM nor does it affect model training. The stock market panic has mixed both concepts: demand for RAM for PCs and servers is not directly affected.
The debate: burst bubble or oversold?
One line of analysis holds that the memory bubble, inflated by speculative AI demand, has burst. They point out that the "picks and shovels" market of a gold rush always ends in a correction. Other experts argue that the drop is an overreaction: TurboQuant only optimizes inference, a smaller segment of total memory consumption, and training needs remain sky-high. Moreover, quantization is not a new technology; Google has simply applied it more efficiently.
The data doesn't lie: a 100-billion-parameter model at fp16 requires 200 GB just for the weights, not counting the cache. TurboQuant reduces cache memory, but not the weights. For personal computers, DDR5 RAM prices remain high (32 GB above 300 euros), and no immediate price drop is expected.
A buying opportunity?
Some analysts see the correction as a chance to buy memory stocks at a discount, arguing that structural semiconductor demand (AI, automotive, IoT) has not disappeared. Others, more skeptical, remind us that memory companies have historically been cyclical and that efficiency innovation always ends up reducing the volumes needed.
The open question is whether TurboQuant is an isolated breakthrough or the start of a trend that will make memory cease to be AI's bottleneck. For now, the market has spoken, but the technology keeps evolving.
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Summary of a discussion on Burbuja.info - Foro de economía, actualidad y política., translated from Spanish and reviewed before publication. Read the full discussion (25 replies).
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