#DeepSeek #nvda #輝達 #llm
輝達股價在2月經歷一波修正，主要反映市場對deepseek低成本LLM模型崛起對算力需求的泡沫疑慮。
deepseek引起市場廣大迴響，一度登上app下載榜首，一方面「低算力、高效能」創舉，打破了大型巨頭過往一直以算力堆疊提供效能更高的LLM模型的AI軍備競賽。另一方面，deepseek發表了獨特的軟體演算能力優化LLM模型。
deepseek如何透過低算力達到如chatgpt等級的AI效能？deepseek在論文中揭示了哪些獨到的演算法展現其軟實力？
其實主要係透過創新的訓練方法、架構設計與資源優化策略
包括：1. 純強化學習（RL）訓練，減少對監督數據的依賴；2. 混合專家模型（MoE）架構，提高計算效率；3. 訓練資源極致優化，低成本硬體與高效流程；4. 蒸餾技術與測試時計算（Test-Time Compute）。
更多關於deepseek的介紹，就讓半導體專家 林嘉洤 教授，和金融行銷專家 馬瑞辰 教授 在影片中告訴你！
NVIDIA’s stock price experienced a correction in February, primarily reflecting market concerns over a potential bubble in computing power demand due to the rise of DeepSeek’s low-cost LLM model. DeepSeek has garnered significant market attention, briefly topping app download charts. On one hand, its “low computing power, high efficiency” innovation disrupted the AI arms race, where tech giants traditionally relied on stacking computational resources to enhance LLM performance. On the other hand, DeepSeek introduced a unique software algorithm optimization for LLM models.
How does DeepSeek achieve ChatGPT-level AI performance with low computing power? What unique algorithms did DeepSeek reveal in its papers to showcase its software prowess?
The key lies in innovative training methods, architectural designs, and resource optimization strategies, including:
Pure Reinforcement Learning (RL) training, reducing reliance on supervised data;
Mixture of Experts (MoE) architecture, enhancing computational efficiency;
Extreme optimization of training resources, leveraging low-cost hardware and efficient processes;
Distillation techniques and Test-Time Compute.
For more insights on DeepSeek, let semiconductor expert Professor Lin Chia-Wei and financial marketing expert Professor Ma Rui-Chen explain it all in the video!
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