DeepSeek là gì? [Giải thích Báo cáo Kỹ thuật] | Multi-Head Latent Attention | Mixture of Experts

DeepSeek Decoded: DeepSeek V3 or DeepSeek r1 is outperforming every LLM in the AI world with its Mixture-of-Experts (MoE) architecture, FP8 training, and Multi-Token Prediction (MTP), making it faster, cheaper, and more efficient than ever!

In this video, I break down DeepSeek V3’s architecture, how it beats OpenAI & Meta models, and why it’s a cost-effective LLM for AI development.

From Multi-Head Latent Attention (MLA) to Auxiliary-Loss-Free Load Balancing, we cover all key innovations of DeepSeek V3 using insights from its research paper.

Research Paper: https://arxiv.org/html/2412.19437v1

Topics Covered in This Video:

1. What is DeepSeek V3, and why is it so powerful?

2. Mixture-of-Experts (MoE) Architecture How does it activate only 37B of 671B parameters?

3. Multi-Head Latent Attention (MLA)—a memory-efficient attention mechanism

4. Multi-Token Prediction (MTP): Speeds up training and inference

5. FP8 Training & Cost Efficiency: How DeepSeek trained for just $5 million

6. Real-world coding & reasoning examples

7. Why is DeepSeek so restricted in global knowledge?

8. Is DeepSeek using OpenAI data & libraries?

DeepSeek-V3 is one of the most advanced and cost-effective open-source AI models, competing with GPT-4o and Claude 3.5. Find out why in this video!

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Upcoming Project Teaser:

Next up, I will explain the difference between DeepSeek r1 and DeepSeek r1 zero, along with their training architecture and reasoning capabilities, and also show how to run DeepSeek r1 locally using ollama in your system as well.

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