The article explores the evolution of large language model (LLM) serving, highlighting significant advancements from pre-2020 frameworks to the introduction of vLLM in 2023. It discusses the challenges of efficient memory management in LLM serving and how vLLM's PagedAttention technique revolutionizes the field by reducing memory wastage and enabling better utilization of GPU resources.
The article discusses the importance of fine-tuning machine learning models for optimal inference performance and explores popular tools like vLLM, TensorRT, ONNX Runtime, TorchServe, and DeepSpeed.
This repository contains scripts for benchmarking the performance of large language models (LLMs) served using vLLM.
A startup called Backprop has demonstrated that a single Nvidia RTX 3090 GPU, released in 2020, can handle serving a modest large language model (LLM) like Llama 3.1 8B to over 100 concurrent users with acceptable throughput. This suggests that expensive enterprise GPUs may not be necessary for scaling LLMs to a few thousand users.
