Running Large Language Models (LLMs) locally for experimentation is easy but running them in large scale architectures is not. It requires businesses looking to intergate LLMs into their critical paths to deal with the high costs and scarcity of GPU/TPU accelerators present a significant challenge. Striking the balance between performance, availability, scalability, and cost-efficiency is a must.

While Kubernetes is a ubiquitous runtime for modern workloads, deploying LLM inference effectively demands a specialized approach. Enter LLM-D a Cloud Native Kubernetes based high-performance distributed LLM inference framework. It's architecture centers around a well-lit path for anyone looking to serve at scale, with the fastest time-to-value and competitive performance per dollar, for most models across a diverse and comprehensive set of hardware accelerators.