News : Mesa 26.2.6 Release Notes

Mesa 26.2.6 is a bug fix release which fixes bugs found since the 26.2.5 release. Mesa 26.2.6 implements the OpenGL 4.6 API, but the version reported by glGetString(GL_VERSION) or glGetIntegerv(GL_MAJOR_VERSION) / glGetIntegerv(GL_MINOR_VERSION) depends on the particular driver being used. Some drivers don’t support all the features required in OpenGL 4.6. OpenGL 4.6 is only available if requested at context creation. Compatibility contexts may report a lower version depending on each driver. Mesa 26.2.6 implements the OpenCL 3.1 API, but the version reported by the CL_DEVICE_VERSION, CL_DEVICE_NUMERIC_VERSION and CL_DEVICE_OPENCL_C_ALL_VERSIONS clGetDeviceInfo queries depends on the particular driver being used. Mesa 26.2.6 implements the Vulkan 1.4 API, but the version reported by the apiVersion property of the VkPhysicalDeviceProperties struct depends on the particular driver being used.

See the official website.

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News : MiniMax-M3.1-Flash: Run Locally !

Run MiniMax-M3.1-Flash locally with Unsloth Dynamic GGUFs. MiniMax-M3.1-Flash is MiniMax’s new 428B MoE multimodal model, with 23B active parameters and 1M context, built for coding and agentic use. Learn how to run MiniMax-M3.1-Flash via Unsloth GGUFs locally with Unsloth Desktop or llama.cpp. Our 129.4GB 1-bit achieves ~89.9% top-1 accuracy; 143.9GB 2-bit reaches 91.5%, and 249.4 GB 4-bit UD-Q4_K_XL 98.0%. On coding, Q2 reaches 91.2% and Q4 97.6%. We weighted quant performance across seven categories: coding, math, chat, tools, multilingual, reasoning and agentic use. Thank you MiniMax for day-zero access.

See this news on the unsloth.ai official webpage.

MiniMax-M3.1-Flash is a native multimodal mixture-of-experts model for coding, agentic workflows, computer use, and cybersecurity. It supports text, image, and video inputs with a context window of up to 1M tokens. Building on MiniMax-M3, M3.1-Flash improves task performance and inference efficiency with sparse attention throughout the model, quantization-aware training, and DSpark speculative decoding.

See the huggingface model webpage.

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