The field of large language models (LLMs) is moving towards more efficient deployment and inference, with a focus on reducing memory footprint and computational cost. Researchers are exploring various techniques such as quantization, activation sparsity, and mixed-precision quantization to achieve this goal. Notably, the development of novel frameworks and algorithms is enabling the native quantization of 4-bit activations, integerized matrix multiplication, and rank-aware sparse inference. These advancements have the potential to significantly improve the efficiency of LLMs, making them more suitable for deployment on edge devices and in resource-constrained environments. Noteworthy papers include BitNet v2, which introduces a novel framework for native 4-bit activation quantization, and R-Sparse, which proposes a training-free activation sparsity approach for advanced LLMs. FineQ is also notable for its software-hardware co-design for low-bit fine-grained mixed-precision quantization of LLMs, achieving higher model accuracy and energy efficiency.
Efficient Deployment of Large Language Models
Sources
BitNet v2: Native 4-bit Activations with Hadamard Transformation for 1-bit LLMs
Low-Bit Integerization of Vision Transformers using Operand Reodering for Efficient Hardware
R-Sparse: Rank-Aware Activation Sparsity for Efficient LLM Inference
FineQ: Software-Hardware Co-Design for Low-Bit Fine-Grained Mixed-Precision Quantization of LLMs
A Summation-Based Algorithm For Integer Factorization
Precision Where It Matters: A Novel Spike Aware Mixed-Precision Quantization Strategy for LLaMA-based Language Models
Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction
Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics