Models & Labs
llama.cpp b9204 Release Adds New Support
llama.cpp Releaseshigh confidence
Why it matters
- →Enhances technical capabilities for developers using llama.cpp.
- →Expands platform compatibility, supporting diverse hardware configurations.
- →Strengthens the existing infrastructure without introducing new models.
The b9204 release of llama.cpp has been announced, featuring support for d_conv=15 in the ssm-conv.cu module. This update is part of the ModalityConditionalAdapters branch and enhances the framework's capabilities for developers. The release maintains compatibility with a broad array of platforms, including macOS, Linux, and Windows, and supports various hardware configurations such as Apple Silicon and Vulkan. This update focuses on improving the technical infrastructure rather than introducing new models.
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What happened next
- 1llama.cpp b9194 Release Expands Platform Support
llama.cpp Releases · May 18, 2026 · Same story
- 2llama.cpp b9394 Release Expands Platform Support
llama.cpp Releases · May 29, 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.
