Worked on the jeejeelee/vllm repository to deliver integration of the AITER MLA attention backend with Eagle3’s speculative decoding on the ROCm platform. Focused on deep learning and GPU programming, the work involved modifying metadata handling and kernel operations to enable single-token decoding and enhance multi-token verification performance. Using Python and leveraging machine learning techniques, the developer established a ROCm-optimized decoding path that improves compatibility and throughput for Eagle3’s speculative decoding. The integration was documented under the ROCm feature tag and prepared for further testing, reflecting a targeted approach to backend optimization and platform-specific performance improvements within the project.