llama.cpp has released build b12119, focusing on stability improvements for its realtime server component. Key updates include sending error events instead of crashing on exceptions and routing websocket handlers through ex_wrapper. The release also enforces stricter validation for tool schemas, rejecting those that cannot be converted by the grammar, and preserves previous tools if the chat template rejects new ones. These changes enhance reliability for developers using the local inference API.
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llama.cpp Releases · September 22, 2026 · Same story
llama.cpp Releases · October 28, 2026 · Same story
llama.cpp b12065 fixes KV cache graph scheduling crash
3 developments
This release quietly patches a gap in llama.cpp's OpenCL backend by adding support for the 'repeat' operation across f16, i32, i16, and bf16 types. For users relying on AMD or Intel GPUs via OpenCL rather than Vulkan or ROCm, this means fewer fallbacks to slower CPU execution for models that use these tensor operations. It’s a targeted fix that improves stability and performance on non-NVIDIA hardware without changing the core inference engine. The update also includes standard binaries for CUDA 13 and ROCm 10.0, keeping the stack current with latest driver ecosystems.
This release quietly closes the hardware gap for non-NVIDIA users by adding default support for ROCm 10.0 on both Linux and Windows, allowing AMD GPU owners to run local models without complex configuration. It also brings CUDA 13.5 builds to Ubuntu and Windows, future-proofing the toolchain for newer NVIDIA architectures while maintaining backward compatibility with CUDA 12. Apple Silicon KleidiAI is disabled in this build, a notable regression for Mac users who rely on that specific optimization. The update solidifies llama.cpp's position as the most versatile inference runtime available, covering everything from Snapdragon NPUs to Intel Arc via SYCL.
This release stabilizes the build pipeline by fixing a threading bug that caused test hangs on self-hosted runners, ensuring more reliable binaries for everyone. More importantly, it brings first-class support for ROCm 10.0 and CUDA 13 across Linux and Windows, closing the gap with NVIDIA's latest driver stacks. AMD users finally get parity with CUDA builds without manual configuration tweaks. The update also expands Apple Silicon coverage to include KleidiAI, though it remains disabled by default in this build. It’s a maintenance-heavy release that quietly modernizes the backend support matrix.
This release finally brings proper asynchronous handling to Model Context Protocol tasks, allowing Claude Code to offload long-running tool calls to the background instead of blocking the main thread. For enterprise users, the new pooled budget rules and JSON logging make the Claude apps gateway significantly more manageable for large teams. The update also tightens error reporting in auto mode, ensuring developers get clear reasons when actions are denied rather than silent failures. It’s a pragmatic patch that improves reliability for complex agent workflows.
© GitHub ChangelogGitHub is finally exposing Copilot code review configuration to the REST and GraphQL APIs, closing a major gap for enterprise automation. Administrators can now programmatically control review effort, enablement, and custom instructions across organizations and repositories, moving beyond manual web interface tweaks. The update also refines the user experience with compact overviews, explicit attribution of AI sources like MCP servers, and better handling of bot-authored PRs. This shift makes Copilot Review a first-class citizen in CI/CD pipelines rather than just a UI feature.
© GitHub ChangelogGitHub finally stops treating chat as a passive afterthought in its enterprise analytics. By adding Copilot Chat and the VS Code Agents window to the impact dashboard, the platform now tracks nine distinct experiences instead of just seven. The rebranding of 'Passive users' to 'Chat-first users' is a semantic correction that aligns metrics with actual developer behavior rather than arbitrary adoption thresholds. This gives engineering leaders a complete picture of how AI is actually being used across their codebases, closing the visibility gap between typing commands and writing code.