CPU inference and quantization
Runs inference on the CPU and adapts to available threads. Supported GGML quantization options include q4, 5.1, 8, and f16.
A native app for managing, verifying, and running AI models locally without a GPU.
Local AI Playground is a free, open-source desktop app for experimenting with AI models offline and in private. It brings model management, download verification, and local inference into one interface, with CPU execution that requires no GPU.
The app provides a quick inference interface for testing models and a local streaming server for connecting them to other AI applications. Its Rust backend is designed for a compact, memory-efficient native application.
Runs inference on the CPU and adapts to available threads. Supported GGML quantization options include q4, 5.1, 8, and f16.
Tracks models in a directory of your choice and sorts them by usage, helping you organize a local collection.
Provides a concurrent downloader with resume support, so multiple model downloads can proceed and interrupted transfers can continue.
Computes BLAKE3 and SHA256 digests and provides a BLAKE3 quick check. A known-good model API, model information cards, and license and usage chips support model inspection.
Starts an inference server in two steps: load a model, then start the server. The website demonstrates using it with window.ai to power another AI application.
Provides an interface for trying inference, configuring inference parameters, and writing output to .mdx files. Remote vocabulary support is also listed.
Experiment with a downloaded model offline using the quick inference interface. This suits local testing where keeping the inference workflow on your computer matters.
Download several models, inspect their information and license labels, and verify file digests before choosing which model to load.
Load a model and start the streaming server to provide inference for another AI application, including the window.ai workflow demonstrated on the website.
The website offers Windows .MSI and .EXE downloads, macOS downloads for M1/M2 and Intel machines, and Linux AppImage and .deb packages.
The listed available inference capability uses the CPU. GPU inference and parallel sessions are listed as upcoming features.
The website lists /audio and /image as upcoming server features, rather than available capabilities.
Usage-based sorting and directory selection are available. Custom sorting and searching, nested directories, and model exploration and recommendations are listed as upcoming features.
Os dados de tráfego são apenas para referência.
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