Open LoRA: A Scalable Fine-Tuned Model Serving Framework

Open LoRA is a highly efficient framework designed for serving thousands of fine-tuned LoRA (Low-Rank Adaptation) models on a single GPU. It optimizes resource utilization by enabling dynamic adapter loading, reducing memory overhead, and ensuring high throughput with low latency. Open LoRA is particularly beneficial for applications that require rapid model switching and efficient inference without deploying separate instances for each fine-tuned model.

Key Features

• Dynamic Adapter Loading: Just-in-time (JIT) loading of LoRA adapters from Hugging Face, Predibase, or custom filesystems.

• Efficient Memory Utilization: Supports merging adapters per request for ensemble inference without preloading all models into memory.

• Optimized Inference: Uses advanced optimizations like tensor parallelism, flash-attention, paged attention, and quantization for improved efficiency.

• Scalability: Supports serving thousands of fine-tuned LoRA models on a single GPU.

• Cost Reduction: Reduces serving costs while maintaining low latency and high throughput.

• Streaming & Quantization: Implements token streaming and quantization for optimized inference.