3 papers
cs.LG2026
Normalized Architectures are Natively 4-Bit
Maxim Fishman, Brian Chmiel, Ron Banner +2
Training large language models at 4-bit precision is critical for efficiency. We show that nGPT, an architecture that constrains weights and hidden representations to the unit hype…
cs.LG2025
FP4 All the Way: Fully Quantized Training of LLMs
Brian Chmiel, Maxim Fishman, Ron Banner +1
We demonstrate, for the first time, fully quantized training (FQT) of large language models (LLMs) using predominantly 4-bit floating-point (FP4) precision for weights, activations…
cs.LG2025
Scaling FP8 training to trillion-token LLMs
Maxim Fishman, Brian Chmiel, Ron Banner +1
We train, for the first time, large language models using FP8 precision on datasets up to 2 trillion tokens -- a 20-fold increase over previous limits. Through these extended train…