What is 'Inference Cost'?

Inference cost

Inference cost in the context of large language models (LLMs) refers to the computational resources required to run the model and generate a response based on a given input. It's essentially the price you pay for using an LLM.

Here's a breakdown of the key factors influencing inference cost:

1. Hardware: LLMs are computationally intensive, typically requiring powerful GPUs for efficient processing. The cost varies depending on the type and number of GPUs used. For example, using a low-end GPU like NVIDIA T4 might cost around $0.6 per hour, while high-end configurations with multiple NVIDIA A100s can reach $45 per hour.

2. Model size: Bigger LLMs generally have higher inference costs due to their complex internal structure and larger number of parameters. Smaller models like Meena may be relatively cheap to run, while the cost of using behemoths like GPT-3 can be significant.

3. Input and output length: The cost also depends on the length of both your input prompt and the generated output. More tokens (roughly corresponding to characters) translate to more processing power needed, driving up the cost.

4. Optimization techniques: Techniques like model quantization, knowledge caching, and efficient inference pipelines can significantly reduce the computational demands and thus the cost. Platforms like Deci AI's Infery-LLM utilize such optimizations to achieve faster and cheaper inference.

5. Cloud vs. on-premises: Deploying LLMs on cloud platforms like AWS or Azure offers flexibility and scalability but can result in higher costs compared to on-premises deployment, where you have more control over the infrastructure but require initial investment.

Understanding these factors can help you choose the most cost-effective approach for your LLM needs. Additionally, research is ongoing to make LLMs more efficient and less expensive to run, potentially leading to broader accessibility in the future.