4 citations · 5 across the 10 of their papers we have counts for
5 papers · 1 filter
What Matters for NVFP4 Training? A Comparative Study of Low-Precision Pretraining Recipes
Anjulie Agrusa, Andrei Panferov, Elizabeth Wei +7
Training large language models directly in 4-bit floating-point (FP) formats promises substantial improvements in throughput and energy efficiency. While some recipe design choices…
Disaggregated Quantization: Specializing LLM Prefill and Decode
Andrei Panferov, Maximilian Kleinegger, Sweta Priyadarshi +2
Prefill and decode reward different approaches to quantization: low-precision arithmetic accelerates prompt processing, while compact weights reduce memory traffic during generatio…
Apertus LLM Family Expansion via Distillation and Quantization
Andrei Panferov, Davit Melikidze, Martin Jaggi +1
The wide adoption of LLMs has led to their use in great variety of applications and scenarios, such as chatbot assistants and data annotation, creating the need for the models to s…
Grid Games: The Power of Multiple Grids for Quantizing Large Language Models
Vage Egiazarian, Erik Schultheis, Andrei Panferov +3
A major recent advance in quantization is given by microscaled 4-bit formats such as NVFP4 and MXFP4, quantizing values into small groups sharing a scale, assuming a fixed floating…
Quartet II: Accurate LLM Pre-Training in NVFP4 by Improved Unbiased Gradient Estimation
Andrei Panferov, Erik Schultheis, Soroush Tabesh +1
The NVFP4 lower-precision format, supported in hardware by NVIDIA Blackwell GPUs, promises to allow, for the first time, end-to-end fully-quantized pre-training of massive models s…