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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 ...
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's TurboQuant reduces the KV cache of large language models to 3 bits. Accuracy is said to remain, speed to multiply. Google Research has published new technical details about its compression ...
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