DeepSeek-V3

DeepSeek-V3 adopts a Mixture of Experts (MoE) architecture with a total parameter count of 671 billion, but only approximately 37 billion parameters are activated per inference.

The instruction-following and context comprehension capabilities of new-generation models such as GPT-4, Claude 3.5, and DeepSeek-V3 have improved significantly, making complex prompt templates no longer necessary.

DeepSeek-V3 has 671B total parameters, but only approximately 37B parameters are activated per inference.

DeepSeek partially addressed MoE engineering challenges in V3 through an auxiliary-loss-free load balancing strategy and a dedicated MoE parallelism framework.

The MoE architecture dynamically selects a small number of experts to participate in computation through a router, achieving sparse activation; mainstream frontier models including GPT-4, Mistral, and DeepSeek all adopt similar designs

The training cost of DeepSeek-V3 is only one-twentieth that of GPT.

2024年底至2025年初，DeepSeek-V3和DeepSeek-R1相继发布

DeepSeek-V3的MoE架构将算力消耗降低了约80%

DeepSeek-V3 was released at the end of 2024.

DeepSeek-V3以极低的训练成本实现了顶尖推理能力

DeepSeek-V3 is the flagship base model released by DeepSeek at the end of 2024.

DeepSeek-V3 has a total of 685B parameters, and even after Q4 quantization its size still exceeds 400GB.

Meta's Llama 3, Alibaba's Qwen2.5, High-Flyer's DeepSeek-V3/R1, Mistral AI's Mistral/Mixtral, and Google's Gemma 2 are all hosted on the Hugging Face platform.

Models with fewer than 7B parameters show a noticeable gap compared to commercial models such as GPT-4o and DeepSeek-V3 in areas like instruction following and long-form reasoning, and are suitable for lightweight tasks such as text classification and simple question answering.

Recently popular models such as DeepSeek-V2/V3 and Qwen2-MoE all adopt the MoE architecture.

Next-generation models such as DeepSeek-V3 and Qwen3 have natively integrated the MTP mechanism during the pre-training stage.

DeepSeek-V3 adopts an auxiliary-loss-free load balancing strategy, automatically learning a reasonable load distribution by introducing a learnable bias term for each expert.

Open-source models such as Meta's LLaMA series, Mistral AI's Mixtral, and DeepSeek's DeepSeek-V3/R1 have gradually approached and in some scenarios even surpassed closed-source commercial models on tasks such as programming and reasoning

Google's Switch Transformer and Mixtral 8x7B are classic representatives of the MoE architecture, and Qwen3, DeepSeek-V3, and others also adopt this architecture.

Open-source large models such as Meta's Llama series, Alibaba's Qwen series, DeepSeek-V3/R1, and the Mistral AI series have reached levels close to or in some respects surpassing closed-source commercial models.