Llama.cpp b9877: Multi-buffer abort prevents crashes

The open-source project llama.cpp, which enables running large language models locally on consumer hardware, has released version b9877. The key change is an immediate abort when a multi-buffer situation is detected, addressing issue #25276. This prevents silent data corruption or crashes that could occur from buffer handling errors in inference pipelines. The release is signed with a GPG key and includes pre-built binaries for a wide array of platforms: macOS (Apple Silicon and Intel), Linux (x64, arm64, s390x), Windows (CPU, CUDA 12/13, Vulkan, OpenVINO, SYCL, HIP), Android (arm64), and even openEuler with ACL Graph. Developers can upgrade to ensure their local inference setups are more robust, especially when running complex models or long contexts that might trigger multi-buffer conditions.

This fix is particularly relevant as the industry increasingly relies on local AI for privacy and offline use. Llama.cpp's popularity (119k stars on GitHub) underscores its role in democratizing AI inference. By patching this edge case, ggml-org continues to refine the software's stability, making it a more reliable choice for developers building desktop or mobile apps that embed language models. The multi-buffer abort is a small but crucial improvement that prevents hard-to-debug failures, especially in production environments where uptime matters.