Abstract: 3D Gaussian Splatting (3DGS) is rapidly gaining popularity for its photorealistic rendering quality and real-time performance, but it generates massive amounts of data. Hence compressing ...

Retrieval-augmented generation (RAG) is a modern method used with large language models (LLMs) to deal with vast volumes of data. Instead of sending all potentially relevant data to an LLM, the RAG ...

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 ...

turboquant-py implements the TurboQuant and QJL vector quantization algorithms from Google Research (ICLR 2026 / AISTATS 2026). It compresses high-dimensional floating-point vectors to 1-4 bits per ...

Random rotation: Multiply the input vector by a fixed random orthogonal matrix. This makes each coordinate follow a known Beta(d/2, d/2) distribution. Lloyd-Max scalar quantization: Quantize each ...

The big picture: Google has developed three AI compression algorithms – TurboQuant, PolarQuant, and Quantized Johnson-Lindenstrauss – designed to significantly reduce the memory footprint of large ...

Even if you don’t know much about the inner workings of generative AI models, you probably know they need a lot of memory. Hence, it is currently almost impossible to buy a measly stick of RAM without ...

Abstract: Historically, the Vector Quantization (VQ) image compression algorithm was designed for single-core processors. Despite its simplicity, impressive bit rates, and good reconstructed image ...

If you’ve used ChatGPT, Perplexity, or any modern AI-powered search engine recently, you've experienced vector search even if you didn’t realize it. Unlike traditional keyword-based search, vector ...