llama.cpp has released build b12398, focusing on Vulkan performance improvements for AMD RDNA3 and RDNA4 GPUs. Key changes include optimized Flash Attention processing, reduced shared memory usage via buffer reuse, and fixes for NVIDIA hangs and sparse FP16 underflow bugs. The release also provides binaries for CUDA 12/13, ROCm 10.0, and various CPU architectures across macOS, Linux, and Windows.
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This release quietly extends llama.cpp’s hardware support to the latest NVIDIA and AMD stacks. By shipping binaries for CUDA 13.5 and ROCm 10.0, it ensures users on cutting-edge GPUs don't have to compile from source to get performance gains. The Vulkan build also fixes header inclusion issues that previously broke compilation on some Linux distributions. It’s a maintenance update rather than a feature leap, but it keeps the local inference runtime compatible with the fastest consumer and datacenter hardware available today.
This is a routine maintenance release for llama.cpp that quietly extends hardware support to the latest NVIDIA and AMD stacks. By adding CUDA 13.5 and ROCm 10.0 binaries, it ensures local inference remains viable on cutting-edge GPUs without waiting for community forks. The build also includes OpenVINO and SYCL variants, reinforcing its role as the universal runtime for diverse accelerators. While no new model architectures are introduced, this update keeps the toolchain compatible with modern driver ecosystems.
This release quietly expands llamafile's reach into enterprise mainframes with Q0 quant support for IBM's s390x architecture. It also updates CUDA builds to version 13.5 and introduces ROCm 10.0, keeping the runtime competitive against NVIDIA's latest drivers. Apple Silicon KleidiAI is disabled in this build, shifting focus toward broader hardware compatibility rather than optimized ARM performance. The inclusion of Snapdragon NPU support on Linux further broadens the edge computing footprint. This is infrastructure maintenance that ensures llamafile remains a viable option for diverse, non-standard hardware environments.
This release prioritizes stability over new features, addressing a critical zstd decompression vulnerability that could have exposed response data. Beyond security, it significantly hardens the reliability of Model Context Protocol (MCP) integrations, fixing persistent connection drops and authentication loops that plagued background tasks. The update also resolves context compaction failures on third-party gateways, ensuring sessions no longer crash when hitting token limits. For developers relying on Claude Code for complex workflows, this patch removes several friction points that previously interrupted automated coding sessions.
© GitHub ChangelogGitHub is quietly removing Claude Haiku 4.5 from its entire Copilot ecosystem by December 2026, a move that forces enterprise and business users to migrate away from Anthropic’s fastest model. While individual annual subscribers retain access, the deprecation signals a strategic shift in GitHub’s default model stack, likely prioritizing higher-margin or more integrated options like GPT-4o or newer Claude variants. This isn't just a sunset notice; it's a mandate for workflow updates, requiring administrators to actively manage model policies rather than relying on passive defaults. The move highlights the fragility of relying on third-party models within closed IDE ecosystems, where access can be revoked unilaterally. Developers must now audit their settings to ensure compliance with new default stacks. This transition marks a clear boundary between individual flexibility and enterprise control in AI tooling.
© GitHub ChangelogThe GitHub Copilot desktop app finally reaches parity with the web interface for enterprise users. Version 1.2.0 makes GitHub Enterprise Server support generally available, allowing developers to connect their GHES accounts directly within the IDE-like environment. This closes a significant gap for organizations that cannot or will not use GitHub.com, enabling remote repository and pull request workflows without leaving the app. While cloud sessions remain unavailable for these instances, local inference on enterprise code is now fully integrated.