DeepNetz is a universal AI runtime designed to run large language, vision, speech, image, and embedding models locally on a wide range of hardware, including CPU, GPU, and NPU. It addresses the challenge of executing massive models on minimal or constrained hardware by automatically selecting the optimal inference backend and optimizing memory usage, allowing users to run models that might otherwise exceed their system's capabilities.
The platform supports ten model types and offers access to a curated catalog of over 130 models. cpp, and Piper, providing day-zero support for new models as they are released. cpp, HuggingFace Transformers, ExLlamaV2, vLLM, CTranslate2, and ONNX Runtime—with DeepNetz automatically choosing the most suitable backend for the user's hardware. Key-value cache compression is implemented to reduce VRAM usage and enable larger context windows, while hardware auto-detection configures quantization levels, batch size, and layer offloading based on detected GPU, VRAM, CPU cores, and RAM.
DeepNetz includes a built-in web UI featuring a responsive chat interface, eliminating the need for separate frontend setup. It also provides native tool calling support compatible with OpenAI-style tool definitions, enabling the creation of agents that interact with external APIs. The platform offers an OpenAI-compatible API, allowing users to switch to DeepNetz by simply changing the base URL in their existing code. Delivery options include downloadable installers for Windows, macOS, and Linux, as well as a Python library installable via pip. Users can interact with DeepNetz through a Python API, command-line interface, web UI, or REST API.
The tool is available for free for personal use, with a Pro version offered for commercial applications. DeepNetz is distributed under the MIT License. Its architecture is designed to sit between user applications and inference backends, handling all aspects of hardware detection, model routing, and optimization automatically.
In the AI & ML space, DeepNetz takes a focused approach. It focuses on running large AI models on local hardware without requiring expensive cloud resources. DeepNetz is a B2B product aimed at developers and ML engineers. DeepNetz follows a freemium model, with paid plans starting at $9. DeepNetz is available on the command line, macOS, Windows, and Linux.
DeepNetz first shipped in 2026. Across PulseGate's embedding index, DeepNetz has few near neighbours, marking it as relatively distinct. Among its 10 catalogued features are Web UI, model catalog, and KV cache compression. It exposes integrations via a public API.
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