DeepSeek's latest large language model delivers outstanding performance at significantly reduced training costs.

Good news reported by Andrew Ng in his latest The Batch newsletter!

"... DeepSeek-V3 is a mixture-of-experts (MoE) transformer that comprises 671 billion parameters, of which 37 billion are active at any moment. The team trained the model in 2.79 million GPU hours — less than 1/10 the time required to train Llama 3.1 405B, which DeepSeek-V3 substantially outperforms — at an extraordinarily low cost of $5.6 million. ..."

From the abstract:

"We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2.

Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models. Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training. In addition, its training process is remarkably stable. Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks. The model checkpoints are available at this https URL."

Tumbling Training Costs, Desktop AI Supercomputer, Tighter AI Export Restrictions, Improved Contrastive Loss

DeepSeek-V3 Technical Report (open access)