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 ...
If you've spent any time running local LLMs, you've probably hit the same wall I have. You find the perfect model quantized to 4-bits, just small enough to fit in your GPU's context window. You then ...
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 ...
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 ...
Unveiled at Google’s annual Next event, the pair showcased using Managed Lustre as a shared cache layer across inference ...
From edge inference to NVIDIA STX, purpose-built KV cache infrastructure for consistent performance at scale. SUNNYVALE, CA / ...
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