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 ...

If Google’s AI researchers had a sense of humor, they would have called TurboQuant, the new, ultra-efficient AI memory compression algorithm announced Tuesday, “Pied Piper” — or, at least that’s what ...

Learn how to understand and compute line integrals in vector fields using both Python and traditional paper methods! This video walks you step by step through the concepts of line integrals, ...

A Data professional with over 4 years of experience building, managing and optimising large data infrastructure. A Data professional with over 4 years of experience building, managing and optimising ...

oLLM is a lightweight Python library built on top of Huggingface Transformers and PyTorch and runs large-context Transformers on NVIDIA GPUs by aggressively offloading weights and KV-cache to fast ...

In today’s data-rich environment, business are always looking for a way to capitalize on available data for new insights and increased efficiencies. Given the escalating volumes of data and the ...