The article discusses the challenges and strategies for load testing and infrastructure decisions when self-hosting Large Language Models (LLMs).
Run:ai offers a platform to accelerate AI development, optimize GPU utilization, and manage AI workloads. It is designed for GPUs, offers CLI & GUI interfaces, and supports various AI tools & frameworks.
This blog post provides a guide for optimizing LLM serving performance on Google Kubernetes Engine (GKE) by covering infrastructure decisions, model server optimizations, and best practices for maximizing GPU utilization. It includes recommendations for quantization, GPU selection (G2 vs A3), batching strategies, and leveraging model server features like PagedAttention.
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.
