Run GGUF LLMs, vision, speech, and stable diffusion completely offline on mobile or Mac.
Offline GGUF text, vision, voice, and image generation with GPU and NPU acceleration.
OGAM is an offline AI suite that runs GGUF language models, Stable Diffusion, and Whisper speech-to-text natively on your mobile device or Mac. It suits professionals who need localized text generation, document analysis, and voice tools without exposing data to external servers.
The app handles on-device text generation using architectures like Llama and Qwen, processes PDFs via local vector embeddings, and executes Stable Diffusion image generation. It manages RAM allocation explicitly and supports local-network servers and tool calling.
It suits mobile-first professionals, translators, and writers who require strict offline operation and hardware acceleration. It does not suit those seeking cloud-hosted scalability or web-based multi-user management.
Utilizes phone GPUs and NPUs via OpenCL and Metal for faster token rates.
Embeds uploaded PDFs and text documents locally in SQLite using a bundled MiniLM model.
Integrates Whisper speech-to-text and multimodal models like SmolVLM directly on hardware.
Provides policy controls to monitor and override memory limits for resident models.
The core app is free under its open-source license. An optional Pro tier adds voice mode, custom personas, tool integrations, and sync for $119 lifetime or $7.99 monthly.
767 open remote AI-training roles, updated November 14, 2026; most pay $20–$175 an hour. Zekai lists them free; the application goes to the employer’s platform.
These reviewed AI tools do the same job instantly — from our scored directory.
No. All text generation, speech transcription, vision analysis, and image creation run locally on your hardware.
Flagship devices with Snapdragon 8 Gen 2/3 or Apple Silicon provide the best token rates and NPU image generation speeds.
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