Using special tags embedded in the output, the model directly links every factual claim it makes to the specific source document or database row it pulled the information from.

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 ...

Companies running large language models face a persistent bottleneck: the memory consumed by key-value caches during ...

The general definition of quantization states that it is the process of mapping continuous infinite values to a smaller set of discrete finite values. In this blog, we will talk about quantization in ...

Model quantization bridges the gap between the computational limitations of edge devices and the demands for highly accurate models and real-time intelligent applications. The convergence of ...

One of the most widely used techniques to make AI models more efficient, quantization, has limits — and the industry could be fast approaching them. In the context of AI, quantization refers to ...

There's a whole world of tools to launch local LLMs out there, and these are some of the best.

Nota AI, a company specializing in AI model compression and optimization, announced that two of its papers on MoE-specific ...