The current developments in the research area are primarily focused on optimizing energy efficiency and performance in GPU-based systems, with a strong emphasis on AI inference and data processing. There is a notable trend towards hardware-software co-designs that aim to maximize the utility of emerging GPU technologies like NVIDIA's Multi-Instance GPU (MIG). These designs often incorporate FPGA-based accelerators and dynamic batching systems to enhance throughput, reduce latency, and improve energy and cost efficiency. Additionally, there is a growing interest in developing unified schemes for GPU offloading, which simplify the process for developers by supporting multiple GPU platforms and providing intuitive interfaces. Another significant area of innovation is in the optimization of deep learning algorithms, where IO-awareness and systematic methods for deriving optimized algorithms are becoming crucial for achieving energy and capital efficiency. This is driven by the increasing importance of transfer costs in GPU energy consumption. Overall, the field is moving towards more integrated and efficient solutions that leverage advanced hardware capabilities and innovative software approaches to address the challenges of high-performance computing and AI inference.