A CodeWeavers engineer opened a merge request yesterday for Wine to use Mesa's Zink OpenGL-on-Vulkan driver by default. This would build Zink as a Windows Portable Executable (PE) for allowing OpenGL to go straight to the Vulkan API with the host Vulkan drivers...
KTransformers 0.5.3 released today for this framework for efficient inferencing and fine-tuning of large language models (LLMs) with a focus on CPU-GPU heterogeneous computing. With this release, KTransformers 0.5.3 is now more applicable for CPUs lacking Advanced Matrix Extensions (AMX) and AVX-512 in now providing some AVX2-only kernels too...
Libinput devised a Lua-based plug-in system for modifying devices/events. The Lua plug-in support was introduced last year with libinput 1.30 but unfortunately some security issues have now come to light with the implementation...
If Valve's latest Steam Survey monthly figures are accurate, Steam on Linux enjoyed a very wild month of March. Steam on Linux is now above the 5% threshold and more than twice the size of the Steam on macOS marketshare...
With Linux 7.0-rc6 having released on Sunday, we are hitting the point of the cut-off of new feature material being allowed into the Direct Rendering Manager's DRM-Next tree of queuing new graphics/display/accelerator feature code ahead of the upcoming Linux 7.1 merge window. As presumably the last AMDGPU/AMDKFD feature pull ahead of Linux 7.1, today's pull request from AMD contains some noteworthy final enhancements...
One of the strengths of Red Hat Ansible Automation Platform is its flexible automation of an array of use cases across ITOps. It includes multiple options to help you jumpstart new automation projects, using Ansible Content Collections. With Ansible Content Collections, you can access more than 200 Red Hat Ansible-certified and validated collections, built and delivered by partners and Red Hat so you can automate more quickly. In this blog post, you’ll learn about new and updated content for some of the most common use cases. So, let’s jump into it!Comprehensive Microsoft Windows automatio
The promise of large language models (LLMs) is clear. From code generation to customer support, from document analysis to creative workflows, organizations everywhere are racing to integrate LLMs into their products and operations. The enterprise LLM market is projected to grow from $6 billion in 2025 to over $50 billion by 2035. But behind the excitement lies a practical challenge—serving LLMs in production can be expensive, inefficient, and operationally complex.The production scale challengesInference cost is the real billThere's a common misconception that training is where most of the m
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