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This article introduces Saluki 27B, a model released by Underdog—a 7.9 GB, 2-bit quantized variant of Qwen3.8-27B—built using the GSQ-RCO method from ISTA-DASLab. It claims to outperform the original 54 GB full-precision model on the BFCL v4 benchmark across 120 tasks; however, all performance conclusions are based solely on the developers’ assertions and have not been independently verified.
The llama.cpp project has released build version b11869, which primarily fixes the parameter-parsing logic for the select and selectattr functions in Jinja templates and provides precompiled binaries that support dozens of hardware–backend combinations across macOS, Linux, Windows, Android, iOS, and openEuler.
llama.cpp v2.45.0 (tag b11868) has been officially released, with the core update being the introduction of a KV-head splitting mechanism in the Hexagon backend to optimize multi-core HMX Flash Attention performance and unify the FA splitting control flag. Binary packages are simultaneously available for all major platforms, including macOS, Linux, Windows, Android, iOS, and openEuler, supporting multiple backends such as CUDA 12/13, Vulkan, ROCm, OpenVINO, SYCL, and Snapdragon (Adreno + Hexagon).
llama.cpp has officially released version b11867 on GitHub. Key changes include a refactored server entry point (PR #31113) and the addition of the server_role mechanism. Additionally, more than 40 precompiled binaries are now available, covering macOS, Linux, Windows, Android, iOS, and openEuler, with support for diverse hardware backends such as CPU, CUDA (12.4/12.8/13.4), Vulkan, ROCm 10.0, OpenVINO, SYCL (FP16/FP32), and Snapdragon (Adreno + Hexagon).
The llama.cpp project has released build version b11865, with core changes including the addition of memory-boundary checks in the RDMA receive logic (PR #30399) and a significant expansion of hardware-acceleration support, covering CUDA 13.4, ROCm 10.0, Vulkan, OpenVINO, SYCL, and Qualcomm Snapdragon—providing full-stack heterogeneous computing backends for CPU, Adreno, and Hexagon. macOS KleidiAI support and openEuler builds have been explicitly marked as DISABLED.
llama.cpp v.b11861 fixes the weight distortion issue in the minimax-m3 model that occurred under NVFP4/MXFP8 quantization due to the scale tensor not being injected into the computation graph, thereby preventing “token soup” and residual vanishing; it also provides multi-platform binary builds covering macOS, Linux, Windows, Android, iOS, and openEuler.
Hugging Face has officially released AnyLanguageModel 1.0, an open-source Swift framework fully compatible with the Apple Foundation Models API. It supports seamless switching between local models (MLX, llama.cpp, Core ML) and hosted models (OpenAI, Anthropic, Gemini, Hugging Face Inference Providers, etc.), and introduces new features such as image input, tool invocation, guided generation, and automatic adaptation of inference chunks.
