What is the relationship between inference speed and model size?

Inference speed generally decreases as model size increases, primarily due to greater computational demands and memory bandwidth requirements. Larger models necessitate more hardware resources and time for processing inputs.

This inverse relationship stems from fundamental factors. Larger models have more parameters to compute during inference, increasing latency significantly. They impose heavier demands on GPU memory and bandwidth, becoming bottlenecks. While optimization techniques like quantization can improve large model speed, smaller models inherently favor low-latency scenarios. The trade-off between model capability (often correlated with size) and speed remains key.

Applications demanding real-time responses, such as chatbots or edge computing devices, typically require smaller, optimized models to meet latency requirements. Conversely, larger models, though slower, are employed for complex tasks like detailed text generation where accuracy supersedes speed needs. Businesses must evaluate this speed-accuracy trade-off carefully for deployment.