DeepSeek-V3 - A high-performance large language model by SiliconFlow, leveraging MoE architecture and Huawei Cloud Ascend collaboration.

## Technical Architecture of DeepSeek DeepSeek-V3 employs a **Mixture-of-Experts (MoE)** architecture with **671 billion total parameters**, of which **37 billion are activated per token**. It integrates **Multi-head Latent Attention (MLA)** and **DeepSeekMoE** designs for efficient training and inference. ## Technical Details of DeepSeek-V3 The model was pre-trained on **14.8 trillion high-quality tokens**, followed by **Supervised Fine-Tuning** and **Reinforcement Learning** stages. Training required **2.788 million H800 GPU hours**, costing approximately **$5.5 million**, significantly lower than comparable models. ## Key Features of DeepSeek-V3 - **Scalability**: 671B total parameters (37B active/token). - **Efficiency**: Optimized for cost-effective inference via MoE. - **Performance**: Benchmarks show parity with leading closed-source models (e.g., GPT-4). - **Collaboration**: Enhanced by Huawei Cloud Ascend’s acceleration engine. ## Accessing DeepSeek-V3 via SiliconCloud Users interact via **SiliconCloud’s OpenAI-compatible API**: ### Citation sources: - [DeepSeek-V3](https://cloud.siliconflow.cn/i/B0Wp0GL7) - Official URL Updated: 2025-04-01