An enterprise data center needs to host high-throughput AI inference applications. The project constraints require a cost-effective accelerator solution that features low thermal design power (TDP) and optimized performance for scaling trained models. Which GPU option is best suited for this application?

Selecting the appropriate GPU accelerator requires balancing workload requirements (inference vs. training), power consumption, thermal limits, and enterprise support. The NVIDIA Tesla T4 is explicitly engineered for scale-out database and AI inference workloads. Built on the Turing architecture, it features dedicated Tensor Cores and operates at a very low thermal design power (TDP) of only 70 watts. This makes it highly power-efficient and allows it to be passively cooled in dense multi-GPU server chassis, keeping operational and cooling costs low. GeForce RTX 4090 and 4060 (Choices A and D) are gaming-oriented GPUs. Aside from EULA restrictions prohibiting their deployment in commercial data centers, they have high power demands (RTX 4090 can draw up to 450W) and utilize active cooling fans that are incompatible with standard front-to-back data center cooling designs. CPU-only servers (Choice C) lack the parallel architecture and specialized Tensor Cores required to meet high-throughput, low-latency requirements for modern AI inference models. Thus, the Tesla T4 provides the optimal balance of power efficiency, cost, and targeted performance.