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Found 7 papers, 5 papers with code
Linear Combination of Saved Checkpoints Makes Consistency and Diffusion Models Better
For example, LCSC achieves better performance using 1 number of function evaluation (NFE) than the base model with 2 NFE on consistency distillation, and decreases the NFE of DM from 15 to 9 while maintaining the generation quality on CIFAR-10.
Evaluating Quantized Large Language Models
Post-training quantization (PTQ) has emerged as a promising technique to reduce the cost of large language models (LLMs).
LV-Eval: A Balanced Long-Context Benchmark with 5 Length Levels Up to 256K
In contrast, the average context lengths of mainstream benchmarks are insufficient (5k-21k), and they suffer from potential knowledge leakage and inaccurate metrics, resulting in biased evaluation.
A Simulation Platform for Multi-tenant Machine Learning Services on Thousands of GPUs
Multi-tenant machine learning services have become emerging data-intensive workloads in data centers with heavy usage of GPU resources.
Characterization and Prediction of Deep Learning Workloads in Large-Scale GPU Datacenters
Modern GPU datacenters are critical for delivering Deep Learning (DL) models and services in both the research community and industry.
Optimizing Network Performance for Distributed DNN Training on GPU Clusters: ImageNet/AlexNet Training in 1.5 Minutes
To address this problem, we propose a communication backend named GradientFlow for distributed DNN training, and employ a set of network optimization techniques.
Distributed, Parallel, and Cluster Computing
Deep Image: Scaling up Image Recognition
We present a state-of-the-art image recognition system, Deep Image, developed using end-to-end deep learning.
