1 citations · 1 across the 1 of their papers we have counts for
2 papers
cs.LG2026★ 1 cited
"Give Me BF16 or Give Me Death"? Accuracy-Performance Trade-Offs in LLM Quantization
Eldar Kurtic, Alexandre Marques, Shubhra Pandit +2
Quantization is a powerful tool for accelerating large language model (LLM) inference, but the accuracy-performance trade-offs across different formats remain unclear. In this pape…
cs.LG2026
Bridging the Gap Between Promise and Performance for Microscaling FP4 Quantization
Vage Egiazarian, Roberto L. Castro, Denis Kuznedelev +8
The recent hardware-accelerated microscaling 4-bit floating-point formats such as MXFP4 and NVFP4, supported on NVIDIA and AMD GPUs, promise to revolutionize large language model (…