llama.cpp has released version b12399, adding pre-built binaries for CUDA 13.5 and ROCm 10.0 across Linux and Windows platforms. The update also includes Vulkan builds that explicitly include SPIRV-Headers to resolve compilation errors on certain distributions. macOS Apple Silicon builds with KleidiAI are currently disabled in this release. This maintains compatibility with the latest GPU architectures without introducing new model support or quantization methods.
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llama.cpp Releases · September 20, 2026 · Same story
llama.cpp Releases · September 20, 2026 · Same story
This release targets the persistent performance gap on AMD hardware by optimizing Vulkan Flash Attention specifically for RDNA3 and RDNA4 architectures. The changes include reducing shared memory usage through buffer reuse and fixing critical stability issues like NVIDIA hangs and sparse FA underflow bugs. While there are no new model architectures, these low-level shader optimizations directly translate to faster inference speeds for local users on modern AMD GPUs. It is a quiet but necessary engineering update that tightens the ecosystem for non-NVIDIA accelerators.
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.