Month: 2025-10. Focus on delivering hardware-accelerated ML deployment capabilities for AMD NPUs using VitisAI. Key feature delivered: Llama 3.1 8B Instruct model optimization and quantization for AMD NPUs, with configuration files and docs guiding setup, environment generation, and deployment. No major bugs reported. Overall impact: enables faster, more cost-effective AMD deployments and expands hardware support; supports end-to-end deployment pipeline. Technologies demonstrated include Llama 3.1 8B Instruct optimization, VitisAI, AMD NPU deployment, model quantization, configuration management, and documentation.

2025-05 Monthly Summary for microsoft/Olive: NVIDIA TensorRT RTX support and optimization workflows were implemented within the Olive framework, enabling hardware-accelerated inference on RTX devices. The work includes new optimization recipes for ViT, CLIP, and BERT models using TensorRT-RTX, and standardization of fp32 to fp16 conversion. Documentation and configuration updates were completed to reflect the new workflows and constants, facilitating easier adoption and deployment.