The rise of DeepSeek has set the tech world ablaze, and as we witness its influence, it’s crucial to discern the three major trends shaping its trajectory. Let’s delve into how to effectively utilize DeepSeek and the impact it’s making.
The Influence of DeepSeek
1. Rivals Tremble, Capital Flocks
On January 20, 2025, the DeepSeek-R1 model was unveiled, and by January 26, it had skyrocketed to the top of the free charts in both the Chinese and US Apple App Stores. This development sent shockwaves through the US capital markets, with NVIDIA’s stock plunging 17% on January 27, wiping out over a trillion dollars in market value. DeepSeek’s ability to train models comparable to its own with a fraction of the graphics cards has effectively shattered the strategic restrictions imposed by the US on China in the field of artificial intelligence. OpenAI’s CEO swiftly reconsidered their open-source strategy, introducing the o3-mini model.
Domestic and international capital has shown immense interest in DeepSeek, with nearly a hundred investment intentions expressed. It’s clear that DeepSeek’s valuation is poised to reach new heights.
2. DeepSeek Goes Open Source, Industries Rush to Deploy
Following DeepSeek’s announcement to open-source R1, industries across the board began integrating or deploying models related to DeepSeek. Major cloud platforms like Huawei Cloud and Baidu AI Cloud, as well as automotive and financial sectors, have all announced collaborations with DeepSeek.
DeepSeek Tears Through the Veil of Computing Power
1. DeepSeek’s Full Version vs. Distilled Version
The full version of DeepSeek, with a size of 671B, employs an innovative MOE architecture that offers faster inference speeds and requires fewer training resources. The distilled versions come in various sizes, smaller and faster but somewhat less capable.
2. Distillation Technique Boosts DeepSeek R1’s Performance
The V3 model significantly improved inference speed compared to historical models. Based on the V3 model, and with the help of a new reward mechanism GRPO and a rule-based validation mechanism, the DeepSeek-R1 model was trained, matching the performance of the GPT-o1 model.
3. DeepSeek’s Innovative Strategies and Their Impact
DeepSeek introduced MLA, uniquely employed the DeepSeek-MoE architecture strategy, and utilized a mixed-precision framework, resulting in rapid computation, low cost, and powerful inference capabilities.
How to Efficiently Use DeepSeek?
1. Various Ways to Utilize DeepSeek and Their Performance
Whether it’s through direct access to the official website or local deployment using Cherry Studio+DeepSeek API, DeepSeek demonstrates its effectiveness. For instance, in solving a math problem from the national college entrance examination, DeepSeek provided the correct answer through step-by-step, in-depth reasoning.
2. Simple Application: DeepSeek + Knowledge Base
Building an investment research framework is showcased as an example of using DeepSeek combined with a knowledge base.
3. Complex Application: DeepSeek + Cursor
More complex tasks, such as a real-world ball collision experiment, can be accomplished with the help of Cursor.
DeepSeek Clears the Path for a New Era
1. Li Fei-Fei’s $50 Replication of the R1 Model
Li Fei-Fei’s team successfully trained a reasoning model comparable to OpenAI’s o1 and DeepSeek-R1 for less than $50 in cloud computing costs.
2. AI Trends Following DeepSeek
Trend One: Small models will become mainstream (large model distillation).
Trend Two: Training with synthetic data will become the norm.
Trend Three: AI models will iterate on their own (a new paradigm of reinforcement learning).
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