You don't always need an RTX 5090 to run useful models ...

Large language models have moved out of the research lab and into engineers’ daily workflow. LLMs serve as reasoning engines ...

Over the past year, local Large Language Models (LLMs) have made a massive leap forward. Today, a 7B parameter model running on a workstation can easily handle serious workloads—from IDE code ...

At the architectural level, Command A+ represents a major evolution from Cohere’s previous dense models. It is a decoder-only Sparse Mixture-of-Experts (MoE) Transformer. While the model houses a ...

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 ...

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 ...

Feedforward neural networks (FFNNs) constitute the foundational architecture underlying modern deep learning systems. This paper presents a comprehensive mathematical derivation of FFNNs, complete ...

The discovery of the integer and fractional quantum Hall effects naturally prompted the question of whether these effects can be realized without a magnetic field. Answering this is fundamentally ...

The electronic quality of graphene has improved significantly over the past two decades, revealing novel phenomena. However, even state-of-the-art devices exhibit substantial spatial charge ...

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 ...