Optimizations & Performance Enhancements

Dynamic Adapter Loading

• Unlike traditional methods where all fine-tuned models are preloaded, Open LoRA loads adapters dynamically, reducing GPU memory usage.

• JIT (Just-in-Time) adapter loading ensures only the necessary adapters are in memory.

Parallel Processing & Merging

• Tensor Parallelism: Spreads computations across multiple GPU cores to accelerate inference.

• Paged Attention: Handles longer sequences efficiently, reducing memory fragmentation.

• Multi-Adapter Merging: Supports inference using multiple LoRA adapters simultaneously for ensemble generation.

CUDA & Low-Level Optimizations

• Flash Attention: Reduces memory bandwidth usage by computing attention efficiently.

• Precompiled CUDA Kernels: Optimized for low-latency execution, minimizing computation overhead.

• Quantization (FP8/INT8): Reduces model size without significant loss in accuracy, improving inference speed.