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llama.cpp adds vision target support for DFlash/Dspark
llama.cpp Releaseshigh confidence
Why it matters
- →Fixes mapping issues for vision targets like Lfm2VlForConditionalGeneration during model conversion.
- →Resolves double rope reorder bugs in LFM2 and LFM2.5 DSpark drafters.
- →Improves stability for speculative decoding with multimodal models.
llama.cpp has released an update addressing specific issues with multimodal model conversion and speculative decoding. The changes allow vision targets, such as Lfm2VlForConditionalGeneration, to properly map to their text model vocabularies during the conversion process. Additionally, a bug causing double rope reorder errors in LFM2 and LFM2.5 DSpark drafters has been resolved. These updates improve stability for users employing advanced decoding techniques with multimodal architectures.
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llama.cpp b11146 adds CUDA 13 and Snapdragon support
The latest llama.cpp build brings immediate relevance to users on bleeding-edge NVIDIA hardware with native CUDA 13.4 support across Linux and Windows, closing the gap for those testing next-gen GPU architectures. More notably, it finally addresses the mobile inference landscape by including a dedicated build for Linux arm64 Snapdragon devices, covering CPU, Adreno GPU, and Hexagon NPU paths. This moves local AI beyond just desktop GPUs into the realm of high-performance edge computing on Qualcomm silicon. While Apple Silicon builds have KleidiAI disabled in this specific release, the expansion to ARM-based mobile NPUs marks a significant shift in where llama.cpp can run efficiently.
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