70 citations · 205 across the 31 of their papers we have counts for
6 papers · 1 filter
FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression
Zhenheng Tang, Xueze Kang, Yiming Yin +11
To alleviate hardware scarcity in training large deep neural networks (DNNs), particularly large language models (LLMs), we present FusionLLM, a decentralized training system desig…
STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs
Peijie Dong, Lujun Li, Yuedong Zhong +8
In this paper, we present the first structural binarization method for LLM compression to less than 1-bit precision. Although LLMs have achieved remarkable performance, their memor…
Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language Models
Peijie Dong, Lujun Li, Zhenheng Tang +4
Despite the remarkable capabilities, Large Language Models (LLMs) face deployment challenges due to their extensive size. Pruning methods drop a subset of weights to accelerate, bu…
Benchmarking and Dissecting the Nvidia Hopper GPU Architecture
Weile Luo, Ruibo Fan, Zeyu Li +3
Graphics processing units (GPUs) are continually evolving to cater to the computational demands of contemporary general-purpose workloads, particularly those driven by artificial i…
ParZC: Parametric Zero-Cost Proxies for Efficient NAS
Peijie Dong, Lujun Li, Xinglin Pan +4
Recent advancements in Zero-shot Neural Architecture Search (NAS) highlight the efficacy of zero-cost proxies in various NAS benchmarks. Several studies propose the automated desig…
BurstGPT: A Real-world Workload Dataset to Optimize LLM Serving Systems
Yuxin Wang, Yuhan Chen, Zeyu Li +11
Serving systems for Large Language Models (LLMs) are often optimized to improve quality of service (QoS) and throughput. However, due to the lack of open-source LLM serving workloa…