Large language models (LLMs) aren’t actually giant computer brains. Instead, they are massive vector spaces in which the probabilities of tokens occurring in a specific order is encoded. Billions of ...
Google researchers have proposed TurboQuant, a method for compressing the key-value caches that large language models rely on during inference. In a preprint, the team reports up to six times lower KV ...
Morning Overview on MSN
Google’s TurboQuant algorithm slashes the memory bottleneck that limits how many AI models can run at once
Running a large language model is expensive, and a surprising amount of that cost comes down to memory, not computation. Every time a model like Gemini or GPT-4 processes a long document or sustains a ...
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