A Decision Tree for Choosing the Right AI Model Across 5 Task Classes
The article discusses a cost-effective approach to selecting the appropriate AI model for different tasks, rather than defaulting to the most powerful (and expensive) model like GPT-4.
Why it matters
This approach can help AI teams significantly reduce inference costs while maintaining acceptable accuracy, which is crucial for scaling AI applications in a cost-effective manner.
Key Points
- 1Quantized Llama-3 70B model can achieve 0.91 F1 score at $0.003/request, while GPT-4 achieves 0.94 F1 at $0.12/request
- 2A 5-node decision tree is proposed to route tasks based on input token count, output determinism, reasoning depth, and latency SLA
- 3Applying the decision tree reduced cost-per-loop from $1.47 to $0.18 with under 3% accuracy delta
Details
The article highlights the significant cost difference between using a powerful but expensive model like GPT-4 versus a more specialized and quantized model like Llama-3 for certain tasks. It proposes a 5-node decision tree to route tasks to the appropriate model based on factors like input token count, output determinism, reasoning depth, and latency requirements. By applying this decision tree, the author was able to reduce the cost-per-loop from $1.47 to $0.18 with only a minor accuracy impact of under 3%. The key message is to optimize for cost-per-correct-answer rather than just cost-per-token, and avoid defaulting to the most powerful (and expensive) model for every task.
No comments yet
Be the first to comment