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# Local Inference Engines & Runtimes Compared 2026
- URL: https://www.stirup.si/local-inference-engines-runtimes-compared-2026-0dbffd39/
- Published: 2026-10-12T01:24:12.000Z
- Updated: 2026-10-12T01:24:12.000Z
- Description: This analysis compares 47 tools for running models locally, focusing on inference engines, runtimes, and gateways. It evaluates hardware support, APIs, and other key attributes based on official documentation.
- Author: Satish Das
- Tags: News, Stirup Research

## Overview of Local Inference Tools

The landscape of local inference tools is diverse, with three primary categories: inference engines, runtimes and managers, and routers and gateways. Each category serves a distinct purpose and should be evaluated separately. Inference engines execute models directly, runtimes and managers handle model downloads and execution, and routers and gateways manage requests and routing between components.

## Key Considerations for Inference Engines

Inference engines are evaluated based on hardware support, API compatibility, and multi-GPU capabilities. Among the 30 inference engines reviewed, 15 document NVIDIA GPU support, 17 document Apple Silicon support, and 21 document an OpenAI-compatible API. These attributes are critical for users with specific hardware constraints or integration needs.

## Hardware Support and API Compatibility

Tools like *candle-vllm*, *ExLlamaV2*, and *vLLM* are noted for their support of NVIDIA GPUs, while *MLX-LM* and *SwiftLM* are highlighted for Apple Silicon compatibility. OpenAI-compatible APIs are documented by several tools, including *llama.cpp* and *text-generation-webui*, enabling integration with applications designed for the OpenAI API.

## Runtimes and Managers

Runtimes and managers are evaluated based on API compatibility, desktop app availability, built-in model libraries, and headless mode support. Six of the 13 runtimes document an OpenAI-compatible API, and several provide desktop applications for ease of use. Tools like *Jan* and *LM Studio* are noted for their built-in model libraries, facilitating model discovery and download.

## Routers and Gateways

Routers and gateways are evaluated on their ability to provide an OpenAI-compatible endpoint, support for local models, and fallback mechanisms. These tools act as intermediaries, ensuring seamless communication between inference engines and user applications.

## License and Platform Considerations

Licensing varies across tools, with most inference engines using Apache-2.0 or MIT licenses. Copyleft licenses, such as AGPL-3.0, are used by *KoboldCpp* and *text-generation-webui*, which impose conditions on distributing modified versions. Platform support is also a key factor, with tools like *MLC LLM* supporting a wide range of platforms, including iOS and Android.

## Conclusion

Selecting the right tool depends on specific hardware, API, and integration requirements. The comparison table and reviews provide a structured approach to evaluating each tool based on official documentation. Users are encouraged to review each tool's documentation and consider their specific needs before installation.

**Source:** [www.promptquorum.com](https://www.promptquorum.com/power-local-llm/local-llm-run-serve-compared?ref=stirup.si). Analysis based on publicly available information; no hands-on testing is claimed.