Turbo LoRA, developed by Predibase, is a novel fine-tuning method for large language models (LLMs) that combines the quality improvements of Low-Rank Adaptation (LoRA) with the high throughput of speculative decoding, achieving a 2-3x increase in text generation speed without sacrificing task-specific response quality. Unlike existing methods that focus solely on either throughput or quality, Turbo LoRA offers both by utilizing a joint fine-tuning strategy that leverages low rank adaptation and speculative decoding to predict multiple tokens in a single step, reducing inference costs and latency. This approach is computationally efficient, introducing minimal additional parameters compared to other speculative decoding methods like Medusa, allowing for simultaneous serving of multiple Turbo LoRA adapters on a single GPU. Turbo LoRA is particularly advantageous for high concurrency applications and is available on the Predibase platform, offering substantial performance enhancements for fine-tuned models.