architecture

Document the core architecture and component interactions of the uzu inference engine.

1.7k|68|Updated Jun 23, 2025

Search Tags:#architecture #performance #session #generator #inference #engine #kernels

Ready to run

npx skills add https://github.com/trymirai/uzu --skill architecture-trymirai

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: architecture
Source: https://github.com/trymirai/uzu/tree/main/agents/skills/architecture
Command: npx skills add https://github.com/trymirai/uzu --skill architecture-trymirai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Document the core architecture and component interactions of the uzu inference engine.

Core Features & Use Cases

Architecture overview: Session, Generator, Encodables, and Kernels describe how they collaborate to deliver high-performance inference across Apple Silicon backends.
Performance-focused design: Emphasizes optimization, consistency, and scalability for development and debugging.
Use Case: Engineers reference the architecture to guide contributions, testing strategies, and backend integration.

Quick Start

Review the Architecture section to align your changes with the architecture principles and performance goals.

Frequently Asked Questions about architecture

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

What is the core architecture of the uzu inference engine?▼

The core architecture of the uzu inference engine consists of the Session, Generator, Encodables, and Kernels. These components collaborate to deliver high-performance inference across Apple Silicon backends.

How do the Session and Generator components interact during inference?▼

The Session and Generator components collaborate with Encodables and Kernels to manage and execute inference tasks. This interaction is designed to ensure high-performance execution and scalability across Apple Silicon backends.

How do I align my development contributions with the inference engine architecture?▼

You align development contributions by reviewing the architecture principles governing the Session, Generator, Encodables, and Kernels. This enforces consistency and performance requirements for backend integration.

Does the inference engine architecture support Apple Silicon backends?▼

Yes, the inference engine architecture supports Apple Silicon backends. The design emphasizes optimization and scalability to deliver high-performance inference specifically across this hardware.

Why is kernel optimization important for the inference engine architecture?▼

Kernel optimization is important because Kernels collaborate directly with the Session and Generator to execute high-performance inference. The architecture enforces strict performance and consistency requirements for development and debugging.