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All SourcesarXiv
#GPU kernels

NanoQuant: Efficient Sub-1-Bit Quantization of Large Language Models

Intermediate
Hyochan Chong, Dongkyu Kim et al.Feb 6arXiv

NanoQuant is a new way to shrink large language models down to 1-bit and even less than 1-bit per weight without retraining on huge datasets.

#post-training quantization#sub-1-bit quantization#binary LLMs

Quartet II: Accurate LLM Pre-Training in NVFP4 by Improved Unbiased Gradient Estimation

Advanced
Andrei Panferov, Erik Schultheis et al.Jan 30arXiv

This paper shows how to train big language models faster and cheaper by using 4-bit numbers (NVFP4) without losing much accuracy.

#NVFP4#FP4 training#quantization-aware training

Towards Automated Kernel Generation in the Era of LLMs

Intermediate
Yang Yu, Peiyu Zang et al.Jan 22arXiv

AI programs called LLMs can now help write the tiny, super-fast pieces of code (kernels) that make GPUs run AI models efficiently.

#LLM-driven kernel generation#GPU kernels#CUDA