Models & Labs
llama.cpp b9726 Release Adds New Features
llama.cpp Releaseshigh confidence
Why it matters
- →The addition of the --agent argument simplifies server operations for developers.
- →Removing redundant web UI naming compatibility streamlines the codebase.
- →Enhanced platform support broadens accessibility for diverse development environments.
The b9726 release of llama.cpp brings several updates, including the addition of a --agent argument for the server, which simplifies command-line operations. This release also removes redundant web UI naming compatibility, aiming to streamline the codebase. It supports a wide range of platforms, including macOS, Linux, Windows, and openEuler, with enhancements for both AMD and NVIDIA GPUs. While no new models are introduced, the update focuses on improving platform versatility and developer accessibility.
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What happened next
- 1llama.cpp b9776 Release Expands Platform Support
llama.cpp Releases · June 24, 2026 · Same story
- 2llama.cpp b9973 Release Expands Platform Support
llama.cpp Releases · July 13, 2026 · Same story
More from llama.cpp Releases
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llama.cpp b12022 adds ROCm 10 and Snapdragon support
This release quietly cements llama.cpp as the universal inference runtime by finally bringing ROCm 10.0 to Linux and Windows alongside CUDA 13.4, effectively closing the hardware gap for AMD users who were previously left behind. The inclusion of native Snapdragon support on Linux marks a significant step toward running local LLMs on ARM-based mobile chips without emulation overhead. While KleidiAI on Apple Silicon is disabled in this build, the broader expansion to diverse accelerators ensures that developers aren't locked into NVIDIA ecosystems anymore.
llama.cpp Releases
Coding Toolscoding
llama.cpp b12023 fixes shared prefix bug
This release addresses a specific inference artifact where decision prefixes were incorrectly bleeding into output embeddings. By capping shared prefixes at the first output position, llama.cpp ensures that option tokens remain distinct for parent and child tasks. This is a targeted fix for multi-step reasoning or voting workflows rather than a performance boost. It stabilizes complex generation patterns without changing the underlying model architecture.
