AITemplate: Meta AI's New Open-Source, Unified Inference Engine
Meta AI's new library for model inference streamlines inference on NVIDIA and AMD GPUs with performance increases using new fusion techniques.
Created on October 3|Last edited on October 3
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Today Meta AI introduced a new open-source AI inference framework called AITemplate, promising that it can give performance increases up to 12x on NVIDIA GPUs and 4x on AMD GPUs, compared with PyTorch's eager mode.
One of AITemplate's great perks is that it's seamless to change your model's inference target to both NVIDIA and AMD GPUs, so no need to worry about which company's products to focus your effort on. This is done by compiling models into self-contained binaries that aren't dependent on any outside libraries, letting it stay much more flexible to work in different environments.
AITemplate was built to make inference easier and more optimized. Your AI models can be optimized with three new optimizations they've included: vertical, horizontal, and memory fusions. With these fusions, various aspects of your model can be combined into more efficient, single operations to be sent to the GPU.
AITemplate is, of course, open-source. This is just the beginning of the framework's development, and a short feature roadmap is also described on their GitHub repository. In the future, the team hopes to support more layer fusion types, more model templates, and other features, so be sure to check back often. A documentation site is also available to help you get started.
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