6 citations · 12 across the 8 of their papers we have counts for
6 papers · 1 filter
Efficient Long Context Fine-tuning with Chunk Flow
Xiulong Yuan, Hongtao Xu, Wenting Shen +10
Long context fine-tuning of large language models(LLMs) involves training on datasets that are predominantly composed of short sequences and a small proportion of longer sequences.…
Memory Offloading for Large Language Model Inference with Latency SLO Guarantees
Chenxiang Ma, Zhisheng Ye, Hanyu Zhao +9
Offloading large language models (LLMs) state to host memory during inference promises to reduce operational costs by supporting larger models, longer inputs, and larger batch size…
BladeDISC++: Memory Optimizations Based On Symbolic Shape
Xiulong Yuan, Xu Yan, Wenting Shen +5
Recent deep learning workloads exhibit dynamic characteristics, leading to the rising adoption of dynamic shape compilers. These compilers can generate efficient kernels for dynami…
Rubick: Exploiting Job Reconfigurability for Deep Learning Cluster Scheduling
Xinyi Zhang, Hanyu Zhao, Wencong Xiao +5
The era of large deep learning models has given rise to advanced training strategies such as 3D parallelism and the ZeRO series. These strategies enable various (re-)configurable e…
Infinite-LLM: Efficient LLM Service for Long Context with DistAttention and Distributed KVCache
Bin Lin, Chen Zhang, Tao Peng +12
Large Language Models (LLMs) demonstrate substantial potential across a diverse array of domains via request serving. However, as trends continue to push for expanding context size…
Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity
Haojun Xia, Zhen Zheng, Yuchao Li +6
With the fast growth of parameter size, it becomes increasingly challenging to deploy large generative models as they typically require large GPU memory consumption and massive com…