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llama.cpp b11020 adds DeepSeek V3.2/V4 parser support
llama.cpp Releaseshigh confidence
Why it matters
- →Fixes parsing errors for DeepSeek V3.2/V4 models in local inference.
- →Updates CUDA and ROCm binaries to latest versions for better hardware support.
- →KleidiAI remains disabled on Apple Silicon, limiting Mac performance gains.
llama.cpp has released version b11020, primarily addressing compatibility with DeepSeek V3.2 and V4 models by adding required message delimiters to the chat parser. The build includes updated binaries for CUDA 13.3 and 13.4, as well as ROCm 10.0 support across Linux and Windows platforms. However, KleidiAI acceleration on Apple Silicon is disabled in this release, and openEuler builds are also marked as disabled. This update ensures functional chat interactions with newer DeepSeek variants while maintaining broad hardware compatibility.
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llama.cpp b11017 adds CUDA 13 and ROCm 10 builds
This release quietly extends llama.cpp's hardware support to the latest NVIDIA and AMD stacks. By shipping native binaries for CUDA 13 and ROCm 10, it ensures compatibility with newer GPU architectures without requiring users to compile from source. The inclusion of both CUDA versions side-by-side is a pragmatic move for enterprise environments managing mixed infrastructure. While no new model formats are introduced, this update keeps the runtime relevant as hardware vendors push their latest drivers.
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llama.cpp b11018 adds CUDA 13 and ROCm 10 builds
This release quietly cements llama.cpp as the universal inference runtime by finally supporting NVIDIA's latest CUDA 13 stack alongside AMD's ROCm 10. For the first time, users on cutting-edge hardware can run local models without being forced into legacy driver versions or waiting for vendor-specific optimizations. The inclusion of both CUDA 12 and 13 binaries side-by-side removes a major friction point for developers managing mixed environments. While no new model architectures are added, this infrastructure update ensures compatibility with the fastest consumer and data center GPUs hitting the market right now.
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llama.cpp b11019 fixes embedded GGUF loading bugs
This release patches a subtle but critical bug where embedding a GGUF file inside another container caused tensor data corruption due to misaligned memory mapping. The fix ensures the data section is aligned relative to the GGUF header rather than the outer file offset, preventing silent errors that could ruin inference results. It also adds support for loading LoRA adapters via FILE pointers and disables mmap with a warning when alignment issues are detected. This stabilizes llama.cpp for complex packaging scenarios where models are bundled within larger archives or custom formats.
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