![]() ![]() On Windows, we support ONNX with DirectML CPU and GPU support. Geekbench ML 0.6 upgrades our internal version of TensorFlow Lite, supporting newer models and improved performance on Android hardware with NPUs using NNAPI. ![]() New Frameworksĭevelopers don’t typically work directly on bare hardware in assembly - abstraction layers and frameworks simplify the process. And, as always, our models and data sets are identical across all supported platforms, making scores comparable. This means you’ll be able to see how machine learning-powered tasks run on your desktop, laptop, or even a server - whether it has new AI-specific hardware or not. With this latest 0.6 preview, Geekbench ML now supports Windows, macOS, and Linux. New Platforms Geekbench ML on Windows 11 Geekbench ML on macOS 14ĪI and ML-related workflows aren’t just confined to mobile, and hardware architecture on desktop and laptop devices is changing to accommodate this shift in computing. This new preview is available on iOS at the Apple App Store, on Android through the Google Play Store, and newly available for macOS, Windows, and Linux through our downloads page. As companies continue to deliver newer, faster, and better AI systems and features, our updated frameworks and models make it possible to compare ML performance across devices and platforms with Geekbench’s well-known usability. Store image classification tags in an SQLite database.The newest preview of Geekbench for ML workloads is now here, delivering several improvements in our testing methodology for even more accurate measurement of real-world performance, as well as support for three entirely new platforms: Geekbench ML is now available on PC, Mac, and Linux.Run an image classification model on the inference thumbnail.Generate a preview thumbnail and encode it as a JPEG.This database is pre-populated with metadata for more than 70,000 photos. Store photo metadata in an SQLite database.Decompress the photo from a compressed JPEG file.This workload performs the following steps for each photo: It uses MobileNet 1.0 to classify photos and a SQLite database to store the photo metadata alongside their tags. The photo organization workload categorizes and tags photos based on objects they contain, allowing users to search their photos by keyword in image organizer apps. This test renders four PDFs in single-core mode and 16 PDFs in multi-core mode. These files contain large vector images, lines, and text. It renders PDFs of park maps from the American National Park Service, with sizes ranging from 897kb to 1.5MB. The PDF render workload opens complex PDF documents using PDFium, which is Chrome's PDF renderer. This test renders eight pages in single-core mode and 32 pages in multi-core mode. libjpeg-turbo and libpng as the image codecs.Anti-Grain Geometry as the 2D graphics rendering library.litehtml as the CSS parser, layout, and rendering engine.Finally, there are image synthesis workloads that execute feature matching and stereo matching, along with a simulation benchmark that simulates particle physics. On top of that, it runs image editing workloads, such as horizon detection, edge detection, and Gaussian blur. The GPU computation benchmark makes use of machine learning workloads such as background blur and face detection to test object recognition capabilities. There are GPU computation tests too, and it can test OpenCL, Metal, and Vulkan. A larger library of images for import tests.HTML examples representative of modern web design standards. ![]() Bigger photos in resolutions captured by modern smartphones (12-48MP).In the case of Geekbench 6, it's the latest iteration of the Geekbench benchmarking suite, and it aims to measure your smartphone's capabilities in the ways that actually matter when it comes to using any of the best phones. ![]()
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