Does inference speed depend on model size?

Yes, inference speed generally depends heavily on model size. Larger models, typically characterized by more parameters, inherently require more computations during prediction, leading to increased latency under the same hardware constraints.

The primary reasons for this dependency are computational complexity and memory bandwidth. Processing each layer in a larger network demands significantly more floating-point operations (FLOPs). Additionally, moving the massive number of model weights and intermediate activations between the processor and memory becomes a major bottleneck. While hardware accelerators like GPUs and TPUs can mitigate this, they also have practical limits to the model sizes they can efficiently handle, and techniques like quantization, pruning, and specialized kernels become essential for optimization.

Implementing larger models requires careful optimization strategies to manage inference latency. This often involves hardware selection, quantization to lower precision formats (e.g., FP16 or INT8), operator optimization, and potentially model compression techniques. Developers must balance the accuracy gains from larger models against the critical need for acceptable prediction times in production deployments such as real-time applications or systems serving numerous concurrent users.