Researchers have developed a system called RequestRouter that improves the efficiency of large language model inference on single GPUs. Instead of using one static configuration for all requests, RequestRouter uses request-level features to select an optimal inference mode per request. This includes options like FP16, quantized inference, and prefix caching. The system was tested on an 8B instruction-tuned language model and achieved a 2.10x mean latency speedup over FP16 and a 0.48x energy ratio on deployment-style workloads. The gains were consistent across multiple evaluations. --- Why it matters: This matters to AI engineers because it shows that simple request-aware routing can significantly improve the efficiency of large language model inference without requiring retraining or modifying the underlying model. This could lead to faster and more cost-effective deployment of these models in real-world applications. Source: https://arxiv.org/abs/2605.23057