llama.cpp build b8338 adds OpenVINO backend + NPU support for prefill + kvcache

The open-source Llama.cpp project, which enables efficient local execution of large language models, has released a major update with build b8338. This release introduces a new OpenVINO backend developed primarily by Intel engineers, allowing AI models to leverage Intel's integrated Arc graphics (iGPUs) and Neural Processing Units (NPUs) for accelerated inference. The update specifically optimizes two critical components: the prefill phase (initial prompt processing) and KV cache operations (memory management for attention mechanisms), which are typically bottlenecks in transformer-based models.

For developers and researchers running models locally, this means significantly faster performance on consumer Intel hardware like the recently announced Core Ultra 255H processor. Early tests suggest 2-3x speed improvements when properly configured, as compute workloads can be intelligently distributed between CPU, GPU, and NPU. The integration represents a strategic move by Intel to make their hardware more competitive in the AI inference space, challenging NVIDIA's dominance in GPU-accelerated AI.

The update is particularly notable because Llama.cpp has become the de facto standard for running quantized models efficiently on consumer hardware. By adding OpenVINO support, the project expands its hardware compatibility while maintaining its reputation for optimization. This could accelerate the trend toward local AI assistants and specialized models that don't require cloud connectivity or expensive dedicated GPUs.