Google TurboQuant Just Makes Memory Obsolete? MU & SNDK Overblown?
$Micron Technology(MU)$ and $SanDisk Corp.(SNDK)$ fell about 7%, $Western Digital(WDC)$ and $Seagate Technology PLC(STX)$ fell 4%. That's all because of TurboQuant.
Google Research has quietly published TurboQuant — a compression algorithm that makes AI inference 8× faster and uses 6× less memory, with zero accuracy loss and no retraining required.
Morgan Stanley is calling it "another DeepSeek moment." The market reacted immediately: memory stocks sold off hard.
Is the panic justified?
TurboQuant only compresses the KV cache — the temporary memory buffer that stores key-value vectors during inference, growing linearly with context length.
It does not touch model weights stored in HBM, and it has zero impact on training workloads. This distinction matters enormously for how you think about the memory trade.
Why this is a big deal for Google?
Google Research originated TurboQuant — giving $Alphabet(GOOG)$ a first-mover deployment advantage in its own cloud infrastructure (GCP) and AI products (Gemini).
Lower inference cost per token directly improves the unit economics of Google's AI services, expanding margins on every Gemini API call.
TurboQuant also accelerates large-scale vector search — a core component of Google Search's AI features and Vertex AI's retrieval workloads.
The efficiency gain means Google can offer longer context windows (competitive moat) without proportional cost increases — widening the gap with rivals who lack this optimization.
Momery stock overblown or demand really decreases?
The fear is if AI needs 6× less memory per workload, demand for HBM collapses.
History suggests efficiency gains in compute don't reduce demand — they expand it. When the cost per AI query drops, hyperscalers reinvest in larger models, longer context windows, and higher query volumes. The "saved" memory simply gets filled by more ambitious workloads. Morgan Stanley explicitly cites this as limiting downside risk to GPU and HBM volumes.
Why $Micron Technology(MU)$ faces additional pressure?
Micron's sell-off isn't purely algorithmic panic. The company simultaneously reported Q2 capex of $5.39B in FY2026 Q1 — up 68% year-over-year. That level of capital commitment amplifies investor anxiety: any softening in AI memory demand expectations creates outsized financial risk for a company this leveraged to the build-out thesis.
How do you view Google’s newly released TurboQuant?
Is this pullback in memory stocks a buy-the-dip opportunity?
Or has the investment thesis fundamentally changed?
Leave your comments to win tiger coins!
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How do you view Google’s newly released TurboQuant?
Is this pullback in memory stocks a buy-the-dip opportunity?
Or has the investment thesis fundamentally changed?
Leave your comments to win tiger coins!
The "Magic": Released on 24 March 26, this algorithm claims to shrink AI memory usage by 6x & boost performance by 8x without sacrificing accuracy.
The panic: Markets worried that if AI needs 80% less memory, demand for chips from Micron & Samsung would evaporate.
The Reality Check: Analysts call this a classic efficiency paradox. Making AI cheaper doesn't kill demand. It makes it explode as companies run more models, larger batches & longer contexts.
Buy the Dip?
Short term pain: Stocks like SK Hynix & Micron fell 3-6% as investors took profits.
Fundamental strength: The core thesis has not changed. Memory is still the primary bottleneck for AI scaling. HBM supply remains tight through 2026.
I am looking at this pullback in Micron as a gift as this is a great time to go bargain hunting.
@Tiger_comments
The key point for me is that TurboQuant only compresses inference-side KV cache, not HBM used for training or model weights. Lower costs typically drive higher usage — meaning more queries, longer context, and larger models. That’s why I see $Alphabet(GOOGL)$ as the biggest winner here, not a signal of collapsing memory demand.
That said, Micron Technology faces extra pressure due to its aggressive capex. I still view this as a short-term digestion phase rather than a broken thesis, and I’d lean toward selectively buying the dip in stronger names.
@Tiger_comments @TigerStars @TigerClub
While the technology significantly reduces the physical memory footprint required for AI, most analysts view this pullback as a "buy-the-dip" opportunity rather than a fundamental breakdown of the investment thesis.
Despite the immediate price drop, several factors suggest the "Memory Supercycle" is not over:
Targeted Scope: TurboQuant primarily targets inference workloads rather than the high-bandwidth memory (HBM) used in the resource-heavy training phase.
Structural Shortages: The broader market is still grappling with a "global memory crisis" driven by capacity reallocation toward AI and geopolitical supply chain disruptions. Analysts at IDC and Morgan Stanley suggest shortages could persist into 2027.
The core thesis—that AI requires massive amounts of high-speed memory—remains intact, but the narrative is shifting from "quantity at any cost" to "efficiency-driven growth."
Training vs. Inference: TurboQuant primarily optimizes inference. The training of next-gen models (GPT-5/6) still requires brutal amounts of raw VRAM that software tricks cannot bypass.
Commodity to Strategic Asset: Memory is no longer a simple cyclical commodity; it is a strategic bottleneck. Cloud giants (Microsoft, Meta, Google) are still paying premiums to secure supply.
Structural Floor: The "Big Three" (Samsung, SK Hynix, Micron) have shown unprecedented supply discipline, keeping prices firm despite software-side optimizations.
Most analysts view this as a buying opportunity rather than a structural collapse. The 3-4% drop in stocks like Micron (MU) and Western Digital (WDC) following the news is seen as a knee-jerk reaction.
The Jevons Paradox: Historically, when a resource becomes more efficient (cheaper), total demand for it actually increases because more people start using it. TurboQuant makes AI cheaper to run, which could explode the volume of AI applications.
HBM Supply Scarcity: Major players like SK Hynix and Micron are already sold out of High Bandwidth Memory (HBM) through 2026. Software optimization cannot "create" physical silicon that is already committed to long-term contracts.
Research vs. Production: TurboQuant is currently a research breakthrough. It takes time to integrate this into global software stacks (like PyTorch or CUDA), meaning no immediate impact on chip orders.
Think of TurboQuant as the "Pied Piper" (from Silicon Valley) of AI memory. It is a state-of-the-art compression algorithm designed to solve the KV Cache bottleneck in Large Language Models (LLMs).
Massive Compression: It compresses memory data by over 6x (down to 3-bit) with zero accuracy loss.
Speed Boost: On NVIDIA H100 GPUs, it can speed up inference (the "thinking" part of AI) by up to 8x.
Why it matters: It allows existing hardware to handle much larger AI tasks, reducing the immediate need for more physical RAM/HBM per query.
2. Memory stocks are very highly valued at this time
3. The investment thesis in memory stocks has not changed at this time
2. 對大模型嚟講，實際總 GPU 記憶體慳得大約 8% 左右。
3. 但 inference 變得平同快得多 → longer context、更大 batch、更複雜應用變得可行 → AI 使用量爆炸（更多公司跑本地模型、更多 RAG、更多 agents）。