DeepSeek-V3, trained on 2,048 NVIDIA H800 GPUs, addresses hardware limitations in scaling large language models through hardware-aware model co-design. Innovations such as Multi-head Latent Attention, Mixture of Experts architectures, and FP8 mixed-precision training enhance memory efficiency and computational performance, while discussions on future hardware directions emphasize the importance of co-design in advancing AI systems.

RecML is a high-performance, open-source library designed for building and deploying large-scale deep learning recommender systems, optimized for Cloud TPUs and GPUs. It offers state-of-the-art model implementations, a user-friendly API, and flexible architecture to support massive datasets while addressing common challenges in recommendation tasks. Additionally, it emphasizes community collaboration and provides tools for efficient training, evaluation, and deployment.