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 ...

What if the future of artificial intelligence wasn’t about building bigger, more complex models, but instead about making them smaller, faster, and more accessible? The buzz around so-called “1-bit ...

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 ...

Fine-tuning large language models (LLMs) might sound like a task reserved for tech wizards with endless resources, but the reality is far more approachable—and surprisingly exciting. If you’ve ever ...

The inherent weaknesses of large language models are reason enough to explore other technologies, such as reinforcement learning or recurrent neural networks. We need to have a frank conversation ...

Since large language models (LLMs) and generative AI (GenAI) are increasingly being embedded into enterprise software, barriers to entry – in terms of how a developer can get started – have almost ...