Models & Labs
llama.cpp b9085 Release Enhances MiMo-V2.5
llama.cpp Releaseshigh confidence
Why it matters
- →Enhancements to MiMo-V2.5 improve processing efficiency with flash attention MMA/tiles.
- →Expanded platform support increases versatility for developers across different systems.
- →Backend optimizations ensure robust performance and usability improvements.
The b9085 release of llama.cpp brings enhancements to MiMo-V2.5, including the addition of flash attention MMA/tiles. This update aligns with the (256, 256) fattn templates, improving processing efficiency. The release also addresses PR feedback and includes backend operations tests to cover new paths. It supports a wide range of platforms, including macOS, Linux, and Windows, making it more versatile for developers. These changes enhance the performance and usability of llama.cpp, particularly for those utilizing MiMo-V2.5.
Read originalThe story around this
Earlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.
What happened next
- 1Llama.cpp b9116 Release Adds MiMo v2.5 Vision
llama.cpp Releases · May 13, 2026 · Same story
- 2llama.cpp b9857 Release Enhances Performance
llama.cpp Releases · July 2, 2026 · Same story
More from llama.cpp Releases
Coding Toolscoding
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.
