Serving large language models (LLMs) at scale presents many challenges beyond those faced by traditional web services or smaller ML models. Cost is a primary concern for LLM inference, which requires powerful GPUs or specialized hardware, enormous memory and significant energy. Without careful optimization, operational expenses can skyrocket for high-volume LLM services.
For instance, a 70 billion parameter model like Llama 70B demands roughly 140GB of GPU memory to load in half-precision, even before accounting for additional memory overhead…
