Inference Performance

Inference performance in large language models (LLMs) focuses on optimizing the speed, efficiency, and accuracy of running pre-trained models, a critical aspect given their computational demands. Current research emphasizes accelerating inference on diverse hardware (CPUs, GPUs, specialized ASICs) through techniques like quantization, tensor decomposition, and optimized parallel processing, often tailored to specific model architectures such as Transformers and their variants. Improved inference efficiency is crucial for deploying LLMs in resource-constrained environments (edge devices, mobile platforms) and for making these powerful models more accessible and cost-effective for broader applications.