H2O GPTe: Balancing Model Cost, Speed, and Accuracy
- Generative AI
- Enterprise h2oGPTe
This lesson walks through the model selection features in H2O Enterprise GPTe's 1.5 series, available in the free trial environment at genai.h2o.ai. It shows how the platform benchmarks open source models like Llama 3.1 and Mixtral alongside connected private models like Claude 3 and GPT-4 on accuracy against estimated query cost, and how hosted model latency can rise under heavy concurrent use while third party API latency stays steady. The lesson explains that the platform automatically routes each question to a model based on these tradeoffs, but users can pin a model or adjust how much they prioritize speed, cost, or accuracy.
Key takeaways
- How H2O benchmarks models like Claude 3, Llama 3.1, and Mixtral on accuracy against estimated query cost
- Why hosted model latency can rise under heavy concurrent use while third party API latency stays steady
- What automatic model routing does by default versus manually pinning a specific model for a chat
- How retrieval augmented questions send far more tokens to a model, which changes the cost tradeoff
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