From the 1 of 10 linked papers with an AI index.
1 citations · 1 across the 4 of their papers we have counts for
6 papers · 1 filter
DarwinLM: Evolutionary Structured Pruning of Large Language Models
Shengkun Tang, Oliver Sieberling, Eldar Kurtic +2
The paper introduces DarwinLM, an evolutionary search method for training-aware structured pruning of large language models that integrates lightweight post‑pruning training to fin…
"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…
Statistically-Lossless Quantization of Large Language Models
Michael Helcig, Eldar Kurtic, Dan Alistarh
Model quantization has become essential for efficient large language model deployment, yet existing approaches present clear trade-offs: methods such as GPTQ and AWQ achieve practi…
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 (…
EvoPress: Accurate Dynamic Model Compression via Evolutionary Search
Oliver Sieberling, Denis Kuznedelev, Eldar Kurtic +1
The high computational costs of large language models (LLMs) have led to a flurry of research on LLM compression, via methods such as quantization, sparsification, or structured pr…
MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence
Ionut-Vlad Modoranu, Mher Safaryan, Grigory Malinovsky +4
We propose a new variant of the Adam optimizer called MicroAdam that specifically minimizes memory overheads, while maintaining theoretical convergence guarantees. We achieve this…