NVIDIA Investment enthusiast$Alphabet - C(GOOG.US)
If every Google investor is of this caliber, I suggest they first go get a degree in artificial intelligence before coming back to trade stocks.
The direct target of TurboQuant is the KV Cache / vector representation memory overhead in LLM inference, not the memory in mobile phones.
Google itself says it's about high-dimensional vector compression for large models and vector search. KV Cache can achieve about 3 bits, memory is reduced by at least 6 times, and running attention logits on H100 can be accelerated up to 8 times. Which part of that mentions phone memory? 😅
The memory requirements of an AI system don't just come from KV Cache. Training, batching, parameter loading, activation, HBM bandwidth, distributed communication, data center storage, RAG indexing, logs, cold data—it's all still there.
The cost per unit of inference decreases, but the total demand for inference may increase.
This is the classic Jevons paradox: increased efficiency doesn't necessarily reduce total resource demand; it might instead cause usage to skyrocket.
Because Google's memory compression data has water in it, it cannot be widely applied, and even less can it affect memory sales. When I saw this news, I added to my position immediately.
If it's really that good at compression, the worst off is Apple🍎.
If 128GB can be used like 1TB, will Tim Cook ever be allowed to retire?
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