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DeepSeek-V3: Pushing the Boundaries of Open-Source Language Models

DeepSeek-V3 is a significant achievement in open-source language models, with innovative features and strong performance. The model has been released by DeepSeek, a Chinese AI firm founded and backed by the Chinese hedge fund, High-Flyer. This post explores its key aspects and impact.

What is DeepSeek-V3?

DeepSeek-V3 is a large Mixture-of-Experts (MoE) language model with 671 billion total parameters, of which 37 billion are activated per token. Built on DeepSeek-V2, it incorporates architectural innovations for efficient inference and cost-effective training. Pre-trained on 14.8 trillion tokens, it underwent supervised fine-tuning and reinforcement learning.

Key Innovations

  • Multi-Head Latent Attention (MLA): Compresses keys and values, reducing memory usage.
  • DeepSeekMoE with Auxiliary-Loss-Free Load Balancing: Finer-grained experts and dynamic load balancing for better specialization.
  • Multi-Token Prediction (MTP): Predicts multiple tokens, densifying training signals and enabling faster decoding.
  • FP8 Mixed Precision Training: Accelerates training and reduces GPU memory usage.

Training Infrastructure

DeepSeek-V3 is trained on 2048 NVIDIA H800 GPUs with NVLink and InfiniBand. DualPipe pipeline parallelism overlaps computation and communication. Efficient cross-node communication kernels utilize bandwidths.

Performance

DeepSeek-V3 excels in language understanding, reasoning, long context tasks, coding, and mathematics. It performs comparably to leading closed-source models and surpasses them in Chinese factual knowledge and some math benchmarks.

Post-Training

The base model is enhanced with supervised fine-tuning and reinforcement learning. It distills reasoning capabilities from DeepSeek-R1 using rule-based and model-based reward models.

Cost-Effectiveness

Training costs are economical due to optimized algorithms, frameworks, and hardware. Pre-training on 14.8T tokens cost 2.664M H800 GPU hours; full training cost 2.788M GPU hours. DeepSeek-V3 is trained on 8-11x less the normal budget of these kinds of models. DeepSeek estimate the training cost $5.5 million USD.

Availability and How to Use

DeepSeek-V3 is on Hugging Face and can run on NVIDIA, AMD GPUs, and Huawei Ascend NPU. Tools include DeepSeek-Infer Demo, SGLang, LMDeploy, TensorRT-LLM, and vLLM. See the GitHub repo for details. DeepSeek-V3 is also available to use via a hosted API - DeepSeek API.

Limitations and Future Directions

While powerful, DeepSeek-V3 has a large deployment unit and room for generation speed improvements. Future work aims to improve architectures, context length efficiency, and explore beyond transformers.

Conclusion

DeepSeek-V3 advances open-source language models with state-of-the-art performance and economical training. It fosters progress in coding tasks and narrows the gap between open-source and closed-source models.

References

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