  Large language models (LLMs) have exploded in popularity due to their new
generative capabilities that go far beyond prior state-of-the-art. These
technologies are increasingly being leveraged in various domains such as law,
finance, and medicine. However, these models carry significant computational
challenges, especially the compute and energy costs required for inference.
Inference energy costs already receive less attention than the energy costs of
training LLMs -- despite how often these large models are called on to conduct
inference in reality (e.g., ChatGPT). As these state-of-the-art LLMs see
increasing usage and deployment in various domains, a better understanding of
their resource utilization is crucial for cost-savings, scaling performance,
efficient hardware usage, and optimal inference strategies.
  In this paper, we describe experiments conducted to study the computational
and energy utilization of inference with LLMs. We benchmark and conduct a
preliminary analysis of the inference performance and inference energy costs of
different sizes of LLaMA -- a recent state-of-the-art LLM -- developed by Meta
AI on two generations of popular GPUs (NVIDIA V100 \& A100) and two datasets
(Alpaca and GSM8K) to reflect the diverse set of tasks/benchmarks for LLMs in
research and practice. We present the results of multi-node, multi-GPU
inference using model sharding across up to 32 GPUs. To our knowledge, our work
is the one of the first to study LLM inference performance from the perspective
of computational and energy resources at this scale.
