GGUF parser vulnerabilities disclosed May 15, 2026 include a critical integer overflow that lets any malicious model file ...
Abstract: The increasing adoption of machine learning at the edge (ML-at-the-edge) and federated learning (FL) presents a dual challenge: ensuring data privacy as well as addressing resource ...
Empowering the world's largest computer vision ecosystem with a unified, one-click NPU hardware standard for building the next generation of real-world AI applications.
Your CPU can run a coding AI—here's why you shouldn't pay for one (as long as you have the patience for it).
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 ...
Abstract: Quantization is one of the efficient model compression methods, which represents the network with fixed-point or low-bit numbers. Existing quantization methods address the network ...
Large language models (LLMs) are increasingly being deployed on edge devices—hardware that processes data locally near the data source, such as smartphones, laptops, and robots. Running LLMs on these ...
Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...