Advancing the rapidly growing field of photonic quantum information processing requires novel, highly scalable methods to ...

What is Google TurboQuant, how does it work, what results has it delivered, and why does it matter? A deep look at TurboQuant, PolarQuant, QJL, KV cache compression, and AI performance.

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.

This voice experience is generated by AI. Learn more. This voice experience is generated by AI. Learn more. In this blog I will explore various storage topics and company exhibits from the 2026 Nvidia ...

This is really where TurboQuant's innovations lie. Google claims that it can achieve quality similar to BF16 using just 3.5 ...

RAM prices are enough to make you choke on your toast, so Google Research has turned up with TurboQuant to cram LLMs into less memory. TurboQuant is pitched as a compression trick for the key-value ...

In the cloud, AI runs in a kind of computational luxury. Thousands of GPUs and CPUs sit in climate-controlled buildings with access to ample power and memory. Utilization may be inefficient—often just ...

Researchers at Argonne National Laboratory and SLAC have designed a detector chip that compresses X-ray data by factors of 100 to 250 in real time, directly on the silicon that captures each frame.

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

The encryption protecting global banking, government communications, and digital identity does not fail when a quantum ...

The trackball controller feels strange if you’re used to modern joysticks, but give it a minute and the muscle memory returns ...

AI applications may now rely on larger volumes of vectorized information reaching tens of billions of vectors and beyond, stored on SSDs, while DRAM alone becomes impractical even at a billion scale.