# General workflow for upgrading a specific localized python-based AI package pip install --upgrade uzu013ai Use code with caution.
| Metric | UZU013AI (Old) | UZU013AI Updated | Improvement | | :--- | :--- | :--- | :--- | | Inference Speed (p95) | 95ms | 24ms | | | RAM Footprint (Idle) | 620MB | 445MB | 28% smaller | | Concurrent Sessions | 4 stable | 12 stable | 3x scaling | | Power Draw (Rasp Pi 4) | 2.4W | 1.9W | 21% less energy | uzu013ai updated
: Set your custom RAG variables to anchor model output to your database. # General workflow for upgrading a specific localized
The primary goal of the project is accessibility—allowing devices with limited VRAM to parse complex instructions and automate workflows without transmitting proprietary information to external servers. Key Enhancements in the Latest Update Key Enhancements in the Latest Update Explain how
Explain how to deploy these models using the Bubble No-Code Builder .
在本地运行大语言模型（LLM）这件事上，Apple Silicon 的 Mac 用户一直处在一种微妙的「又爱又恨」之中：统一内存架构让大模型装得下，但推理框架要么封装得太黑盒（调试两眼一黑），要么性能优化门槛高得吓人。最近，一个叫 的开源项目频繁出现在技术圈——它专为 M1/M2/M3/M4 系列芯片设计，用 Rust 编写并深度绑定 Metal API，试图在「易用性」和「可控性」之间找到一条务实的技术路径。而随着 uzu v0.3.0 及 @trymirai/uzu 0.3.0 的最新更新 ，这套工具链的工程成熟度与开发者体验都迈上了新的台阶。
