The b9835 release of llama.cpp has been announced, focusing on expanding platform support rather than introducing new features. This update includes ROCm 7.2 support for Ubuntu x64, enhancing options for AMD GPU users. The release covers a wide range of platforms, including macOS, Linux, Windows, and openEuler, providing developers with extensive deployment flexibility. While the update is incremental, it reinforces llama.cpp's adaptability across various systems.
Read originalThe b9831 release of llama.cpp marks a significant enhancement with the addition of DFlash, which brings sliding window attention per layer types. This update is particularly beneficial for developers on macOS, Linux, and Windows, as it extends the tool's compatibility and functionality across these platforms. With ROCm 7.2 now available on Ubuntu, AMD GPU users gain a more robust option for local inference. While no new models are introduced, this release solidifies llama.cpp's role as a versatile inference runtime, especially for those not reliant on NVIDIA hardware. The update also includes various platform-specific improvements, making it a comprehensive upgrade for developers.
The b9832 release of llama.cpp introduces a new debugging capability with the --dump-prog option in jinja, co-authored by Sigbjørn Skjæret. This enhancement is designed to streamline the debugging process for developers. The update also extends compatibility across various systems, including macOS, Linux, Windows, and openEuler, ensuring developers can work seamlessly in their preferred environments. While the release doesn't bring new models or quantization techniques, it reinforces llama.cpp's role as a flexible tool for developers. With ROCm 7.2 and CUDA 12 and 13 support, the platform continues to cater to a broad spectrum of hardware configurations. This update is a testament to llama.cpp's commitment to improving developer experience.
The latest b9833 release of llama.cpp focuses on refining the MiniCPM5 parser, addressing several technical aspects to improve its functionality. This update includes the addition of a new tool call parser, refactoring of the PEG parser, and adjustments to the Jinja min/max API for better compatibility with Jinja2. The release also reverts some shared mapper changes to maintain strict JSON parsing for tool-call arguments. These enhancements aim to streamline the parsing process, ensuring more reliable and efficient handling of XML tool calls and grammar triggers.
The vLLM v0.24.0 release marks a significant update with extensive contributions from 256 developers, introducing support for new models like MiniMax-M3 and DiffusionGemma. This version enhances performance with optimizations such as the FlashInfer sparse index cache and improved throughput for DeepSeek-V4. The update also expands the Model Runner V2 capabilities, supporting quantized models by default and integrating GraniteMoE. These advancements make vLLM more robust and versatile, offering developers improved tools for model deployment and performance tuning.
© TechCrunch AIBase44, a vibe coding platform acquired by Wix, is launching its own AI model, Base1, to enhance app creation through natural language. This move aims to improve latency, cost, and efficiency by integrating the model into its tech stack, setting it apart from competitors relying on external models. The decision reflects a broader trend where AI companies leverage proprietary data and infrastructure for defensibility. While Base44's model is still new, it represents a strategic shift towards specialization in a competitive landscape dominated by frontier AI labs.
© The Verge AIOpenAI is stepping into the hardware space with a new device tailored for its AI-powered coding tool, Codex. In collaboration with Work Louder, known for their mechanical keyboards and macro pads, OpenAI is set to launch a device that promises to enhance Codex shortcuts. The teaser suggests a device similar to Work Louder's Creator Micro 2, which features customizable mechanical switches and a joystick. This move could streamline coding workflows by integrating physical controls with AI capabilities, marking a novel intersection of hardware and AI in coding environments.