The Llama 3.1 70Bmodel, with its staggering 70 billion parameters, represents a significant milestone in the advancement of AI model performance. This model’s sophisticated capabilities and potential ...

Essentially all AI training is done with 32-bit floating point. But doing AI inference with 32-bit floating point is expensive, power-hungry and slow. And quantizing models for 8-bit-integer, which is ...

Large language models (LLMs) aren’t actually giant computer brains. Instead, they are massive vector spaces in which the probabilities of tokens occurring in a specific order is encoded. Billions of ...

The best kinds of research are those that test new ideas and that also lead to practical innovations in real products. It takes a keen eye to differentiate science projects, which can be fun but which ...

New leaks suggest that Intel’s upcoming Nova Lake-S desktop processors will feature an upgraded Neural Processing Unit with around 74 TOPS of INT8 inference capability — positioning the company to ...

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 ...

Sponsored Feature: Training an AI model takes an enormous amount of compute capacity coupled with high bandwidth memory. Because the model training can be parallelized, with data chopped up into ...