LLM Inference Cost per Output Token Measure
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A LLM Inference Cost per Output Token Measure is a LLM performance measure that evaluates the computational cost associated with generating each output token during the inference process of a large language model (LLM).
- Context:
- It can (often) be influenced by factors such as Model Complexity, Hardware Efficiency, and Optimization Strategies.
- It can help organizations understand the economic feasibility of scaling up their LLM deployments.
- It can range from being relatively low for smaller, optimized models to higher for larger, more complex models.
- It can guide decisions on selecting appropriate Cloud Computing Services or On-Premises Infrastructure.
- It can impact the overall cost-efficiency of applications like Large-Scale Text Generation or Personalized Content Delivery.
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- Example(s):
- Counter-Example(s):
- LLM Training Cost per Token Measure, which evaluates costs during the training phase instead of inference.
- LLM Energy Consumption per Token Measure, focusing on energy use rather than financial cost.
- See: Compute Cost per Token, Energy Consumption per Token, Scalability Cost per Token