January 2026: Delivered speculative decoding support for vLLM on Google Kubernetes Engine (GKE), enabling faster online inference via n-gram and EAGLE methods. Created and published deployment configurations, resource specifications, and end-to-end examples; updated documentation to support deployment and validation workflows. There were no major bugs fixed this month. Overall, the work enhances platform performance, scalability, and ease of adoption for advanced decoding strategies, delivering measurable business value through faster inference and efficient resource usage.

November 2024 monthly summary: Delivered end-to-end AI workload acceleration on Google Cloud Platform via Dynamic Workload Scheduler (DWS) integration with Gemma Fine-Tuning in the ai-on-gke project. Implemented GPU-aware batch processing with dedicated A100/H100 GPU pools and integrated Kueue/DWS to optimize scheduling for large-scale AI workloads. Completed infrastructure hygiene improvements, including Terraform formatting fixes and platform script updates, enabling reliable, reproducible deployments and smoother operations for future AI workloads.