Episode 40: DeepSeek facts vs hype, model distillation, and open source competition
Jan 31, 2025
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In this engaging discussion, Kate Soule, Director of Technical Product Management at Granite, Chris Hay, Distinguished Engineer and CTO of Customer Transformation, and Aaron Baughman, IBM Fellow and Master Inventor dive into the realities behind DeepSeek R1. They debunk myths surrounding its hype and discuss the true implications of model distillation for AI competition. The trio explores the evolving landscape of open-source AI and how recent advancements can reshape industry strategy, shedding light on efficiency and innovation in model training.
The $5.5 million cost to train DeepSeek R1 reflects only part of model development, often omitting extensive preparation and data collection necessities.
DeepSeek's introduction of model distillation facilitates the creation of efficient student models from robust teacher models, enhancing AI innovation and accessibility.
Deep dives
Debunking the $5.5 Million Myth
The claim that training state-of-the-art models like DeepSeek R1 costs around $5.5 million has sparked significant debate, and it's essential to understand the context behind this figure. While this number reflects a specific iteration of training a base model, it overlooks the extensive preparation, including months of practice and data collection, necessary to achieve performance in real-world applications. The costs associated with actual model development include various factors, such as hardware, research, and earlier training phases, which can far exceed the cited amount. As a result, presenting the $5.5 million figure without considering these caveats is misleading and does not accurately depict the true expenses of model development in artificial intelligence.
Reinforcement Learning as a Game-Changer
DeepSeek's introduction of reinforcement learning (RL) represents a significant leap forward in training efficiency and model capabilities. The model showcases how RL can enhance the output of existing base models with limited additional data, showcasing the potential to reduce reliance on massive pre-training. By combining RL training with quality structured data, DeepSeek has illustrated that it is possible to achieve impressive results more efficiently, making fine-tuning more viable for smaller datasets. This shift allows for a broader range of organizations to harness powerful AI capabilities without the heavy costs associated with traditional model training.
Distillation and the Future of AI Models
Model distillation is now recognized as a crucial technique for developing smaller models that can maintain high performance without the prohibitive costs of their larger counterparts. DeepSeek's R1 model exemplifies this, allowing users to leverage the power of a robust teacher model to create effective student models optimized for specific tasks. Unlike earlier notions that equate distillation solely with model compression, it now appears as a means of knowledge transfer that can enhance various model architectures. This development signifies a shift toward a new era where the synergy of big models and efficient smaller variants will guide future innovation in AI.
The Impact of Open Source on Competitive Dynamics
The release of DeepSeek's scientific advancements in the open-source domain is transforming the competitive dynamics within the AI industry. By providing access to high-quality models with flexible licensing, DeepSeek potentially diminishes the competitive advantages previously held by larger corporations like OpenAI and Google. This democratization of technology allows smaller organizations and independent researchers to innovate rapidly, creating diverse applications tailored to their needs. Consequently, as open-source models proliferate, the incentives to invest exclusively in proprietary large models may decline, prompting organizations to reconsider their approaches to AI development and their strategies in the competitive landscape.
Let’s bust some early myths about DeepSeek. In episode 40 of Mixture of Experts, join host Tim Hwang along with experts Aaron Baughman, Chris Hay and Kate Soule. Last week, we covered the release of DeepSeek-R1; now that the entire world is up to speed, let’s separate the facts from the hype. Next, what is model distillation and why does it matter for competition in AI? Finally, Sam Altman among other tech CEOs shared his response to DeepSeek. Will R1 radically change the open-source strategy of other tech giants? Find out all this and more on Mixture of Experts.
00:01 – Intro
00:41 – DeepSeek facts vs hype
21:00 – Model distillation
31:21 – Open source and OpenAI
The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity.
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