What if AI could match enterprise-grade performance at a fraction of the cost? In this episode, we dive deep into DeepSeek, the groundbreaking open-source models challenging tech giants with 95% lower costs. From innovative training optimizations to revolutionary data curation, discover how a resource-constrained startup is redefining what's possible in AI.

🎯 Episode Highlights:

• Beyond cost-cutting: How DeepSeek matches top-tier AI performance

• Game-changing memory optimization and pipeline parallelization

• Inside the technology: Zero-redundancy training and dependency parsing

• The future of efficient, accessible AI development

Whether you're an ML engineer or AI enthusiast, learn how clever optimization is democratizing advanced AI capabilities. No GPU farm needed!

References for main topic:

1. [2401.02954] DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

2. DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

3. [2405.04434] DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

4. [2412.19437] DeepSeek-V3 Technical Report

5. https://arxiv.org/abs/2501.12948

6. https://www.deepspeed.ai/2021/03/07/zero3-offload.html

7. [1910.02054] ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

8. [2205.05198] Reducing Activation Recomputation in Large Transformer Models

9. [2406.03488] Seq1F1B: Efficient Sequence-Level Pipeline Parallelism for Large Language Model Training