Benchmarking LLMs: A Deep Dive into Local Deployment & Optimization

Running a local Large Language Model (LLM) presents significant advantages in terms of data security and utilizing AI on private datasets, with performance benchmarking resembling SSD benchmarking due to various influencing factors like model architecture and concurrent requests. Optimizing latency and throughput is crucial, especially when deploying an LLM for chatbots, where a balance between speed, reading efficiency, and GPU cost-effectiveness is essential. OpenAI charges for API usage based on tokens, while open-source alternatives like ollama provide easier entry points. Despite NVIDIA's advancements with their optimized NIMs, which can be quickly deployed locally, challenges persist in scaling inference performance with consumer-grade GPUs, such as the RTX 4090, which are limited by VRAM capacity. Renting such systems can be cost-effective for handling large volumes of tokens, but higher-end models demand significantly more GPU VRAM, presenting a barrier to running more complex models.