Google’s TurboQuant Compression May Support Faster Inference, Same Accuracy on Less Capable Hardware
Google Research unveiled TurboQuant, a novel quantization algorithm that compresses large language models’ Key-Value caches ...
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 ...
Within 24 hours of the release, community members began porting the algorithm to popular local AI libraries like MLX for ...
From edge inference to NVIDIA STX, purpose-built KV cache infrastructure for consistent performance at scale. SUNNYVALE, CA / ...
Nvidia researchers have introduced a new technique that dramatically reduces how much memory large language models need to track conversation history — by as much as 20x — without modifying the model ...
On March 24, 2026, Google Research announced a new suite of compression techniques for large-scale language models and vector search engines: TurboQuant, PolarQuant, and Quantized ...
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