A Guide to LLM Inference Performance Monitoring

Large Language Models (LLMs) are becoming increasingly popular, and choosing the right one for your needs is crucial for the success of your generative AI strategy. One important aspect to consider when evaluating LLMs is their inference performance, which measures how quickly they generate responses. This guide explores LLM inference performance monitoring, including how it works, the metrics used to measure an LLM's speed, and how some popular models on the market perform. LLM inference involves two stages: a prefill phase where input tokens are processed and converted into vector embeddings, and a decoding phase where output tokens are generated one at a time until reaching a stopping criterion. The most important LLM inference performance metrics are latency and throughput. Latency measures how long it takes for an LLM to generate a response, while throughput provides a measure of how many requests the model can process or how much output it can produce in a given time span. Some challenges associated with measuring LLM inference include lack of testing consistency, different token lengths per model, and lack of data. To compare popular LLMs on these metrics, various benchmark tests have been conducted by organizations like Artificial Analysis, GPT For Work, and Predera. These tests provide valuable insights into the performance of different models under varying conditions. In conclusion, while inference performance monitoring is an important factor to consider when selecting an LLM, it should not be the sole determinant. Researching how a language model performs at various benchmarking tests can also help identify the best LLM for your specific needs.

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