llama.cpp has released version b11040, a standard binary update focusing on platform compatibility and crash prevention. The release adds CUDA 13 support (versions 12.8 and 13.3) for Linux and Windows, alongside ROCm 10.0 builds for AMD GPUs. A key change is the disabling of KleidiAI optimizations on macOS Apple Silicon, which were previously enabled by default. The update also includes fixes for allocation failure checks in ggml to prevent runtime crashes. No new model formats or UI features are included in this iteration.
Read originalThis release quietly closes the hardware gap for local inference by adding default builds for CUDA 13 and ROCm 10.0. NVIDIA users on newer driver stacks can finally run without workarounds, while AMD GPU owners get parity with the latest ROCm version. Apple Silicon support is explicitly disabled in this build, a notable regression for Mac users who need to wait for the next patch. The inclusion of OpenVINO and SYCL builds further cements llama.cpp as the universal runtime for diverse hardware, ensuring no major accelerator is left behind.
This release prioritizes stability over new features, addressing critical correctness issues in vector handling for GET_ROWS operations. By fixing vec4 alignment checks and updating CUDA libraries to versions 12.8 and 13.3, it ensures reliable performance across NVIDIA hardware on both Linux and Windows. The inclusion of ROCm 10.0 builds further solidifies AMD GPU support without requiring complex configuration. While no new model architectures are added, these fixes prevent silent corruption in local inference tasks that could otherwise go unnoticed.
This release finally closes a gap in how llama.cpp handles sliding window attention patterns during model conversion. Previously, loaders silently ignored array-based SWA configurations from models like OLMo2 and Gemma3n, relying on hardcoded defaults that masked potential precision loss. The new Model-Saver now explicitly writes per-layer SWA flags and MLA geometry, ensuring bit-exact roundtrips for a dozen architectures including Plamo3 and Cohere2. This matters because it guarantees that converted GGUF files preserve the exact inference behavior of their original checkpoints, eliminating silent degradation for complex attention mechanisms.
This release stabilizes Claude Code by patching a cascade of crashes and session hangs that plagued recent versions. The most notable functional shift is the fallback to AGENTS.md when CLAUDE.md is absent, aligning with broader industry standards for agent configuration. Gateway improvements allow better proxy handling for egress-bound environments, while numerous fixes address edge cases in file editing, plugin management, and resume functionality. It’s a maintenance-heavy update that restores reliability rather than introducing new capabilities.
Anthropic quietly fixed a cost leak in Claude Code’s auto mode. By defaulting to the server-side classifier for API and enterprise users, the update eliminates charges for classifier overhead that previously bled into session costs. This shift means developers no longer pay double for the same logic, while still retaining the ability to opt out via environment variables if needed. The change is a subtle but necessary correction to pricing transparency in automated coding workflows.
© GitHub ChangelogGitHub Copilot’s code review tool has reached general availability, shifting from experimental to a core part of the pull request workflow. The biggest leap is auto-resolution: Copilot now validates whether its own suggestions were actually fixed by subsequent commits and closes them out automatically, saving developers from manual cleanup. It also groups findings into clear states like 'Resolved' or 'Previously missed,' giving a real-time health check of the PR rather than just a static list of errors. This reduces context switching significantly, letting engineers focus on new issues while the AI handles the administrative burden of closing old ones.