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 Layer

• 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.

Attribution Engine

• Automatically records which models, adapters, and data were used for each inference.

• Ensures fair and verifiable attribution to all contributors (developers, data providers, compute nodes).

• Enables reward distribution based on real-time usage tracking.

OpenLedger Network

• Decentralized infrastructure that connects storage, inference, and attribution components.

• Uses smart contracts for access control, attribution logging, and token-based rewards.

• Ensures secure, scalable, and trustless coordination across the AI pipeline.