Sign up to save your podcasts
Or
Share Episode 61: DeepSeek Models Explained - Part II
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
Episode 61: DeepSeek Models Explained - Part II
What if AI could be 95% cheaper? Discover how DeepSeek's game-changing models are reshaping the AI landscape through breakthrough innovations. Journey through the evolution of AI optimization, from GPU efficiency to revolutionary attention mechanisms. Learn when to use (and when to avoid) these powerful new models, with practical insights for both individual users and businesses.
Key highlights:
How DeepSeek achieves dramatic cost reduction through technical innovation
Real-world implications for consumers and enterprises
Critical considerations around data privacy and model alignment
Practical guidance on responsible implementation
References:
Dario Amodei — On DeepSeek and Export Controls
Bite: How Deepseek R1 was trained
[2501.17161] SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
[2405.04434] DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
[2408.15664] Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts
[2412.19437] DeepSeek-V3 Technical Report
[2501.12948] DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
View all episodes
By Saugata ChatterjeeWhat if AI could be 95% cheaper? Discover how DeepSeek's game-changing models are reshaping the AI landscape through breakthrough innovations. Journey through the evolution of AI optimization, from GPU efficiency to revolutionary attention mechanisms. Learn when to use (and when to avoid) these powerful new models, with practical insights for both individual users and businesses.
Key highlights:
How DeepSeek achieves dramatic cost reduction through technical innovation
Real-world implications for consumers and enterprises
Critical considerations around data privacy and model alignment
Practical guidance on responsible implementation
References:
Dario Amodei — On DeepSeek and Export Controls
Bite: How Deepseek R1 was trained
[2501.17161] SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
[2405.04434] DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
[2408.15664] Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts
[2412.19437] DeepSeek-V3 Technical Report
[2501.12948] DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning