DeepSeek-V3 demonstrates that thoughtful hardware-software co-design can overcome the scaling challenges of large language models. By integrating innovations like Multi-head Latent Attention (MLA), Mixture of Experts (MoE) architectures, FP8 mixed-precision training, and a Multi-Plane Network Topology, DeepSeek-V3 achieves cost-effective training and inference at scale. This paper delves into these advancements and discusses future directions for AI hardware and architecture co-design.
DeepSeek-V3 demonstrates that thoughtful hardware-software co-design can overcome the scaling challenges of large language models. By integrating innovations like Multi-head Latent Attention (MLA), Mixture of Experts (MoE) architectures, FP8 mixed-precision training, and a Multi-Plane Network Topology, DeepSeek-V3 achieves cost-effective training and inference at scale. This paper delves into these advancements and discusses future directions for AI hardware and architecture co-design.
Read the full paper here: https://arxiv.org/abs/2505.09343