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cs.LG2026
An Interpretable Latency Model for Speculative Decoding in LLM Serving
Linghao Kong, Megan Flynn, Michael Peng +3
Speculative decoding (SD) accelerates large language model (LLM) inference by using a smaller draft model to propose multiple tokens that are verified by a larger target model in p…
cs.LG2025
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 (…
cs.LG2024
"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…