DeepSeek-V3 New Paper is coming! Unveiling the Secrets of Low-Cost Large Model Training through Hardware-Aware Co-design

A newly released 14-page technical paper from the team behind DeepSeek-V3, with DeepSeek CEO Wenfeng Liang as a co-author, sheds light on the “Scaling Challenges and Reflections on Hardware for AI Architectures.” This follow-up to their initial technical report delves into the intricate relationship between large language model (LLM) development, training, and the underlying hardware infrastructure. The paper moves beyond the architectural specifics of DeepSeek-V3 to explore how hardware-aware model co-design can effectively address the limitations of current hardware, ultimately enabling cost-efficient large-scale training and inference.

https://arxiv.org/pdf/2505.09343

The rapid scaling of LLMs has exposed critical bottlenecks in current hardware architectures, particularly concerning memory capacity, computational efficiency, and interconnect bandwidth. DeepSeek-V3, trained on a cluster of 2048 NVIDIA H800 GPUs, serves as a compelling case study demonstrating how a synergistic approach between model design and hardware considerations can overcome these limitations. This research focuses on the interplay…