Cost-Effective Real-Time Inference on GPU

Quick Answer

Meeting a sub-100ms latency requirement for real-time inference calls for a persistent, dedicated compute resource that's always ready to respond immediately, which is what a SageMaker real-time endpoint provides, as opposed to options built for asynchronous or batch-style processing where some delay before a response is acceptable. Since the model fits on a single GPU, there's no need to reach for a larger, more expensive multi-GPU instance; the ml.g4dn.xlarge, with its single NVIDIA T4 GPU, provides enough compute to serve the model within the latency requirement while being one of the lower-cost GPU instance types available, which is exactly what being most cost-effective while meeting the latency requirement is asking for. Choosing a bigger or newer GPU instance would likely also meet the latency bar but at a higher cost with no real benefit, since the model doesn't need the extra capacity, while choosing a CPU-based instance would risk missing the sub-100ms target altogether for a GPU-sized model. The pattern worth internalizing is that when a scenario specifies a firm low-latency requirement alongside a model that fits comfortably on a single GPU, the answer is usually the smallest, cheapest GPU-backed real-time endpoint that can still meet that latency bar, not the largest available instance.