LLM Inference Time per Token Measure
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A LLM Inference Time per Token Measure is a LLM performance metric that evaluates the average time it takes for a large language model (LLM) to generate or process each token during inference.
- Context:
- It can (often) impact the user experience in applications requiring rapid responses.
- It can be influenced by factors like Model Size, Hardware Configuration, and Optimization Techniques.
- It can help identify Bottlenecks in the inference process and guide efforts to optimize model performance.
- It can be critical for applications with strict latency requirements, such as Autonomous Vehicles or Financial Trading Systems.
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- Example(s):
- Counter-Example(s):
- LLM Inference Cost per Output Token Measure, ...
- Training Time per Token Measure, which evaluates the time taken during the training phase rather than inference.
- Energy Consumption per Output Token Measure, which focuses on the energy usage instead of time.
- See: Compute Cost per Token, Memory Usage per Token, Scalability Cost per Token