Hangzhou Depth Seeker has launched an efficient open source language model with 67.1 billion parameters, using a hybrid expert architecture that excels at handling math, coding and multilingual tasks.
DeepSeek-V3DeepSeek-V3 is a powerful artificial intelligence technology from Hangzhou DeepSeek Artificial Intelligence Basic Technology Research Co.open source macromodel. With 67.1 billion parameters, the model employs a mixed-expertise (MoE) architecture designed for efficient inference and cost-effective training. deepSeek-V3 performs well in several benchmarks with innovative load-balancing strategies and multi-token prediction goals, demonstrating excellent performance especially in math, coding, and multilingual tasks. Its relatively low training cost of approximately $5,576,000, much lower than competing products, demonstrates DeepSeek's significant progress in cost control and technology development.
DeepSeek-V3 combines multiple capabilities such as Natural Language Processing (NLP), Computer Vision (CV) and Speech Processing, and is capable of understanding and generating data in multiple forms such as text, images and audio. Its goal is to promote the popularization and innovation of AI technologies through open source.DeepSeek-V3 is positioned as a general-purpose multimodal intelligence platform designed to provide developers, researchers, and enterprises with powerful tools to build a wide variety of AI applications.
DeepSeek-V3 is based on the Transformer architecture with a multimodal fusion design. It mainly includes a text encoder, an image encoder, an audio encoder and a multimodal fusion module. The text encoder is used to process textual data and is based on a variant of BERT or GPT; the image encoder is based on Vision Transformer (ViT) or Convolutional Neural Network (CNN); the audio encoder is based on WaveNet or a similar architecture; and the multimodal fusion module fuses textual, image, and audio representations to produce a unified output.
The strengths of DeepSeek-V3 are its huge number of model parameters, efficient MOE architecture, low training cost, excellent inference capability, and open source and friendly developer environment. However, it also has some shortcomings, such as the complexity of the MOE architecture, the possible bias in expert selection, the high requirements on the amount of training data, and the high hardware requirements.
DeepSeek-V3 withDeepSeek-R1The main differences are reflected in the model positioning, architecture and parameters, training methods, application scenarios and performance performance and other aspects, the following is a specific comparative analysis:
| DeepSeek-V3 | DeepSeek-R1 | |
|---|---|---|
| model positioning | Generic large language model focusing on scalability and efficient processing | Reasoning-first models focused on handling complex reasoning tasks |
| Architecture and Parameters | Using the Mixed Expert (MoE) architecture, the total number of parameters is up to 671 billion, but only 37 billion parameters are activated for each inference | Based on the Transformer architecture, the number of parameters reaches tens of billions (between 1.5 and 70 billion) |
| Training methods | The main use of mixed-accuracy FP8 training is divided into three phases: high-quality training, extended sequence length, and post-training for SFT and knowledge distillation | Focuses on chain of thought COT inference, R1-zero uses mainly reinforcement learning, DeepSeek-R adds a supervised fine-tuning SFT phase |
| application scenario | Suitable for large-scale natural language processing tasks such as conversational AI, multi-language translationand content generation, etc. | For tasks requiring deep reasoning such as academic research, problem solving applications and decision support systems |
| performance | Excellent performance in math, multilingual tasks, and coding tasks with a maximum output token limit of 8K | Performs better in the Logical Thinking benchmark with a maximum output token count of 32K |
| Other features | Supports ultra-long contexts (up to 128K Token windows), specializes in document analysis, long conversations, and other scenarios, and can integrate visual, speech, and other multimodal inputs (additional configuration required) | Provides multiple distillation versions for developers of different sizes with lower API call costs |
Open source address:https://github.com/deepseek-ai/DeepSeek-V3
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