LLM Model Serving Performance: Latency and Throughput
Main Ideas:
- Machine learning practitioners focus on two measurements for model serving performance: latency and throughput.
- Latency is defined by the time it takes to generate a single token, while throughput is defined by the number of tokens generated per second.
- A single request to the deployed endpoint may not reflect the true throughput capacity of the language model.
- In order to accurately measure throughput, multiple parallel requests need to be sent to the endpoint simultaneously.
- Understanding both latency and throughput is crucial for effectively deploying and optimizing large language models.
Author’s Take:
When deploying large language models, measuring both latency and throughput is essential for optimizing performance. A single request may not accurately reflect the model’s true capacity, so sending multiple parallel requests is necessary to measure throughput accurately. Machine learning practitioners should consider both these metrics to effectively deploy and optimize language models for better performance.