# 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.


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