Advanced reasoning models like DeepSeek-R1 are enhancing AI's ability to solve complex problems by reasoning through intricate logic and providing explainable, step-by-step solutions, but their detailed reasoning processes result in slow throughput, making them less practical for real-time applications. To address this, Predibase introduced Turbo LoRA and Turbo Speculation, techniques that enhance inference speed by predicting multiple tokens in parallel, thus maintaining output quality while reducing latency and GPU costs. These methods allow reasoning models to become viable for real-time applications such as AI-powered customer support and healthcare assistants. Turbo Speculation exploits predictable patterns in reasoning outputs, achieving up to a 2x increase in speed without sacrificing accuracy, and offers significant cost savings and performance improvements by optimizing GPU resource utilization.