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Google unveiled TurboQuant, a method that cuts the memory bottleneck slowing large AI models
Companies running large language models face a persistent bottleneck: the memory consumed by key-value caches during ...
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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 ...
GPU memory (VRAM) is the critical limiting factor that determines which AI models you can run, not GPU performance. Total VRAM requirements are typically 1.2-1.5x the model size due to weights, KV ...
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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