
PrismML has demonstrated the capability to run a 27 billion parameter model on an iPhone, marking a significant achievement in mobile AI processing. This development highlights the potential for deploying large-scale AI models on consumer devices, expanding the accessibility and application of AI technologies.
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© Lev SelectorAnaconda has acquired Kilo Code, expanding its capabilities in data science and AI development.
© Lev SelectorNous Research, an open-source AI lab, has raised $75 million at a $1.5 billion valuation.
© Lev SelectorVulkan and Mojo are emerging as competitors to Nvidia CUDA, enabling LLMs to run efficiently on diverse hardware.
The b10057 release of llama.cpp targets critical SYCL-related issues, enhancing the stability of its computation kernels. A significant fix addresses a row calculation error when K_QUANTS_PER_ITERATION is set to 1, ensuring more accurate results. Additionally, the update corrects the processing of reordered q5_k kernels, which is crucial for maintaining performance integrity. These improvements, contributed by Intel's Todd Malsbary, are designed to bolster the accuracy and efficiency of SYCL operations. While no new features are introduced, the release strengthens the existing framework, providing developers with a more reliable environment for SYCL-based computations.
The latest b10063 release of llama.cpp continues its trend of broadening platform compatibility, now including support for Vulkan on Ubuntu and Windows, as well as ROCm 7.2 on Ubuntu. This update ensures that developers working across diverse hardware configurations can leverage llama.cpp's capabilities more effectively. Notably, the release maintains its focus on providing robust support for both CPU and GPU environments, including CUDA and OpenVINO. While no groundbreaking features are introduced, the expansion of supported platforms signifies llama.cpp's commitment to being a versatile tool for AI developers.
The latest b10064 release of llama.cpp continues its trend of broadening platform compatibility, now supporting a wide array of systems including macOS, Linux, Windows, and openEuler. Notably, this update includes support for Vulkan on Ubuntu and Windows, as well as ROCm 7.2 on Ubuntu, which enhances GPU utilization options for developers. While KleidiAI support on macOS Apple Silicon is disabled, the release still offers a comprehensive suite of builds for various architectures. This update solidifies llama.cpp's position as a versatile inference runtime, catering to a diverse range of hardware configurations.