llama.cpp adds multi-row batching for DeepSeek-OCR
DeepSeek-OCR users can now process entire tables in one go instead of row-by-row...
Deep Dive
The latest llama.cpp release, tagged b10285, adds multi-row batching for DeepSeek-OCR, weaving rows in one shot instead of processing each row individually. The release includes builds for macOS, Linux, Windows, Android, openEuler, and UI assets.
Key Points
- Multi-row batching for DeepSeek-OCR reduces tabular data processing time by 6-8x
- Supports 21+ platforms including CUDA 12/13, ROCm 7.2, Vulkan, and iOS/macOS Apple Silicon
- Co-authored commit [b10285] verified with GitHub's cryptographic signature
Why It Matters
Accelerates document processing workflows for developers using DeepSeek-OCR with large tabular datasets, cutting costs and latency for production AI systems.