The recent advancements in the field of large language model (LLM) optimization and deployment have focused on enhancing inference efficiency, reducing computational costs, and improving hardware utilization. Key innovations include novel batching strategies, performance-aware memory allocation techniques, and runtime optimizations that address data inefficiencies in deep learning training. Additionally, there is a growing emphasis on developing unified inference engines capable of handling hardware heterogeneity and workload complexities, as well as leveraging edge computing for collaborative inference tasks. Notably, the integration of early exit mechanisms and task offloading strategies in distributed systems has shown promising results in balancing response delay and inference accuracy. These developments collectively aim to make LLM deployment more practical and cost-effective, particularly in real-world, resource-constrained environments.
Optimizing LLM Inference and Deployment Efficiency
Sources
Multi-Bin Batching for Increasing LLM Inference Throughput
Towards Performance-Aware Allocation for Accelerated Machine Learning on GPU-SSD Systems
Code generation and runtime techniques for enabling data-efficient deep learning training on GPUs
IterNorm: Fast Iterative Normalization
GUIDE: A Global Unified Inference Engine for Deploying Large Language Models in Heterogeneous Environments
PyPOD-GP: Using PyTorch for Accelerated Chip-Level Thermal Simulation of the GPU
Harpagon: Minimizing DNN Serving Cost via Efficient Dispatching, Scheduling and Splitting
MoE-CAP: Cost-Accuracy-Performance Benchmarking for Mixture-of-Experts Systems
Collaborative Inference for Large Models with Task Offloading and Early Exiting