The decision to deploy a model to production relies on a balance of performance, feasibility, and reliability.

1.  **Feasibility (Memory Use):** Memory usage is often a hard constraint. A model may have excellent accuracy and low latency, but if its memory footprint exceeds the available hardware resources (VRAM/RAM), it cannot be deployed.
2.  **Reliability (Failure Rate):** Failure rate dictates system stability. A model that is fast and accurate but crashes or errors frequently is generally unsuitable for production environments.

Because these metrics are unknown, it is impossible to determine if either model is viable for deployment. Choosing based solely on accuracy and latency ignores the risk that the "best" performing model might be technically impossible to run or too unstable to use.

**Final Answer:**
Defer the decision. It is not possible to recommend a model for production without evaluating memory usage (to verify infrastructure feasibility) and failure rates (to verify system reliability).