KTransformers effectively lowers the threshold of large-scale language modeling through innovative localized deployment solutions. The framework is deeply optimized for mainstream consumer-grade hardware, and supports running all kinds of large-scale models in an ordinary desktop environment equipped with 24GB of video memory and 150GB of RAM, which fundamentally solves the problem of expensive and hard-to-find professional GPU clusters. Compared with traditional deployment methods, this lightweight solution can save more than 80% of hardware investment costs.
The deployment process is designed to be extremely simple: a simple git clone command to get the codebase, install the dependencies in requirements-local_chat.txt, and execute the standard python setup.py install to complete the setup of the base environment. For API service deployment is even easier, just run a single line command to start the industry standard RESTful interface services.
The framework also provides detailed resource configuration guidance, through editing the config.yaml file can be flexibly adjusted video memory, memory occupancy parameters, to support the user according to the actual hardware conditions for accurate optimization. This progressive deployment solution allows small and medium-sized teams without professional operation and maintenance capabilities to easily manage large model applications.
This answer comes from the articleKTransformers: Large Model Inference Performance Engine: Extreme Acceleration, Flexible EmpowermentThe
