The biggest memory burden for LLMs is the key-value cache, which stores conversational context as users interact with AI ...
That much was clear in 2025, when we first saw China's DeepSeek — a slimmer, lighter LLM that required way less data center ...
Researchers have developed a holographic data storage approach that stores and retrieves information in three dimensions by ...
A more efficient method for using memory in AI systems could increase overall memory demand, especially in the long term.
Google has published TurboQuant, a KV cache compression algorithm that cuts LLM memory usage by 6x with zero accuracy loss, ...
Google's TurboQuant algorithm compresses LLM key-value caches to 3 bits with no accuracy loss. Memory stocks fell within ...
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