System Architecture
Core Components
The Open LoRA system is built on a modular architecture consisting of:
LoRA Adapters Storage
Stores fine-tuned LoRA adapters in OpenLedger
Adapters are loaded dynamically when needed rather than preloading all into memory.
Model Hosting & Adapter Merging Laye
Uses a shared base model, while LoRA adapters are merged on-the-fly during inference.
Supports ensemble merging of multiple adapters to improve inference performance.
Inference Engine
Implements efficient CUDA optimizations, including:
Flash-Attention for reducing memory overhead.
Paged-Attention for efficient handling of long sequences.
SGMV Optimization (Sparse General Matrix Vector multiplication) to accelerate inference.
Request Router & Token Streaming
Routes API requests dynamically based on required adapters.
Streams generated tokens efficiently using optimized kernel implementations.
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