7 Ways to Speed Up Inference of Your Hosted LLMs

Companies, from small startups to large corporations, want to utilize the power of modern LLMs and include them in the company’s products and infrastructure. One of the challenges they face is that such large models require a huge number of resources for deployment (inference). Accelerating model inference is an important challenge for developers. It is related to reduced fees for computing resources and the application response speed. <blockquote> “In the future, every 1% speedup on LLM inference will have similar economic value as 1% speedup on Google Search infrastructure.” — Jim Fan, NVIDIA senior AI scientist </blockquote> The development of LLMs and the infrastructure around them is evolving at an unthinkable rate. Every week, new approaches emerge to speed up or compress models. In such a flow of information, it’s hard to keep a finger on the pulse and have an idea of what techniques really work, not just on paper. I tried to understand what improvements are available now for implementation in the project and how much they allow to accelerate the inference of LLM models. <a href="https://betterprogramming.pub/speed-up-llm-inference-83653aa24c47">Website</a>