October 2025: Delivered NPUW KV Cache Optimization and Accuracy Enhancements for openvino. Implemented prefix KV cache reuse across generation calls, reducing inference time. Refined KV cache handling and 3D position ID processing to improve accuracy by avoiding KV cache restoration/storage for partial chunks and correcting chunk prefill inference. All changes align with openvino repository standards and prepared for review.

August 2025 performance summary: Delivered cross-repo NPU-focused enhancements to dynamic LoRA and VLM support across OpenVINO core and GenAI, enabling flexible fine-tuning on NPU hardware and reducing VLM startup latency. Implementations include dynamic LoRA loading with pre-allocated L0 tensors and VLM chunk prefill for NPUW, ensuring correct input handling for 3D VLM workloads and parity with CPU/GPU behavior. These changes improve model adaptability, throughput, and production readiness on NPU-backed inference and fine-tuning pipelines.

In July 2025, delivered two high-impact NPU-focused enhancements across openvino.genai and openvino, significantly improving runtime stability and performance for NPU-backed inference. The work emphasizes reliability, latency, and memory efficiency for long prompts and dynamic/static shape handling on NPUs.