Local AI Playground

Local AI Playground (local.ai) is a native, open-source platform for offline AI experimentation and inference management. It enables running AI models locally without GPU requirements, offering a compact, memory-efficient Rust backend and a suite of features to manage, verify, and run models across CPU environments on Mac, Windows, and Linux.

Key Capabilities

• Local AI inference with CPU support and adaptive threading
• Model management: centralize, organize, and track AI models from various directories
• Digest verification: robust integrity checks using BLAKE3 and SHA256
• Inference server: quick start a local streaming server for model inferencing
• Lightweight, offline-first design: <10MB installer footprint on supported platforms
• Cross-platform: MSI, EXE, AppImage, .deb packages for Windows, macOS, and Linux
• Upcoming features: GPU inferencing, nested directory sorting/searching, advanced model exploration

How to Use Local AI Playground

1. Install the appropriate package for your OS (MSI/EXE for Windows, AppImage for Linux, .deb for Debian-based systems, or native macOS installer).
2. Launch the app and start a 2-click inference session with WizardLM 7B (example) to begin running local models.
3. Manage models: add directories, verify digests, and monitor usage with the centralized Model Management module.
4. Start the Inference Server to enable streaming-style model inferencing locally.

Note: The platform emphasizes privacy and offline operation, with no need for cloud calls or GPU hardware.

Features and Modules

• Local CPU-based inference with adaptive threading
• Centralized Model Management: organize and track models from any directory
• Digest Verification: verify integrity using BLAKE3 and SHA256
• Usage-based, resumable download and directory-agnostic organization
• Inference Server: quick-start local streaming server for model inferencing
• Lightweight footprint: small installers (<10MB on supported platforms)
• Cross-platform packaging: MSI, EXE, AppImage, and .deb options
• Privacy-first: offline operation with no mandatory cloud dependency
• Upcoming enhancements: GPU inference, nested directory handling, enhanced model discovery and recommendation

What’s Included (License & Source)

• Built with a Rust backend for performance and memory efficiency
• Source code licensed under GPLv3
• All rights reserved, with a focus on local, offline AI experimentation