Large language models (LLMs) aren’t actually giant computer brains. Instead, they are massive vector spaces in which the ...

A paper from Google could make local LLMs even easier to run.

Learn why Google’s TurboQuant may mark a major shift in search, from indexing speed to AI-driven relevance and content discovery.

TL;DR: Google developed three AI compression algorithms-TurboQuant, PolarQuant, and Quantized Johnson-Lindenstrauss-that reduce large language models' KV cache memory by at least six times without ...

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 ...

This voice experience is generated by AI. Learn more. This voice experience is generated by AI. Learn more. On March 24, 2026 Amir Zandieh and Vahab Mirrokni from Google Research published an article ...

Within 24 hours of the release, community members began porting the algorithm to popular local AI libraries like MLX for Apple Silicon and llama.cpp.

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 ...

Abstract: Vector quantization (VQ) is a fundamental research problem in image synthesis, which aims to represent an image with a discrete token sequence. Existing studies effectively address this ...

TAMPA, Fla. — Remnants of Vector Launch have made it back to one of its original architects after Phantom Space bought launch assets that were sold off in 2020 during the small rocket developer’s ...

The model is pre-trained on 25T tokens using a Warmup Stable Decay learning rate schedule with a batch size of 3072, a peak learning rate of 1e-3 and a minimum learning rate of 1e-5. The NVFP4 ...

Integrates dynamic codebook frequency statistics into a transformer attention module. Fuses semantic image features with latent representations of quantization ...