Large language models (LLMs) aren’t actually giant computer brains. Instead, they are effectively massive vector spaces in which the probabilities of tokens occurring in a specific order is ...

Google researchers have published a new quantization technique called TurboQuant that compresses the key-value (KV) cache in large language models to 3.5 bits per channel, cutting memory consumption ...

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The biggest memory burden for LLMs is the key-value cache, which stores conversational context as users interact with AI ...

Memory is the faculty by which the brain encodes, stores, and retrieves information. It is a record of experience that guides future action. Memory encompasses the facts and experiential details that ...

At 100 billion lookups/year, a server tied to Elasticache would spend more than 390 days of time in wasted cache time.

Every conversation you have with an AI — every decision, every debugging session, every architecture debate — disappears when ...

Google Research recently revealed TurboQuant, a compression algorithm that reduces the memory footprint of large language ...

Surprisingly, a report out of Korea seeds the idea that Micron will be first to market with stacked GDDR memory.

A paper from Google could make local LLMs even easier to run.