OpenAI's nightmare: Deepseek R1 on a Raspberry Pi

Summary

OpenAI's dominance in the AI space is challenged by DeepSeek, a Chinese startup that created the R1 model, which outperforms OpenAI's best models using far fewer resources. DeepSeek's model, costing just $6 million, can even run on affordable hardware like Raspberry Pi with an external GPU. While larger models still require significant resources, this breakthrough suggests that running powerful AI models at home is now feasible. The AI industry is in flux, with companies like Nvidia seeing massive stock drops, and the energy consumption of AI models being questioned.

Takeaways

• 😀 OpenAI's dominance in AI is being challenged by DeepSeek, a new AI startup from China, with a model that outperforms OpenAI's best models on many metrics.
• 😀 DeepSeek created R1, an open-weights model that cost only $6 million to develop, using GPUs with half the memory bandwidth of OpenAI's hardware.
• 😀 Despite the excitement over R1's capabilities, the model is still far from perfect and requires significant GPU resources to run at full potential.
• 😀 DeepSeek's model can optimize other models to run better on slower hardware, making it possible to run advanced AI models on lower-cost systems like Raspberry Pis.
• 😀 OpenAI's competitive advantage is based on its massive energy and GPU resource requirements, but that advantage weakens when affordable hardware can run strong AI models.
• 😀 While the full version of DeepSeek's 671b model needs substantial GPU power, smaller models like 14b can run on less powerful systems, even on a Raspberry Pi.
• 😀 By using external graphics cards, the performance of AI models can be significantly increased, with tests showing speeds of up to 50 tokens per second.
• 😀 GPUs, particularly those with VRAM, vastly outperform CPUs and system memory in running AI models, allowing for faster processing and better efficiency.
• 😀 This new wave of powerful, affordable AI hardware is opening up possibilities for ARM and RISC-V systems, with custom PCs now able to support GPUs.
• 😀 The AI market is in a bubble, with Nvidia's stock plummeting by more than $500 billion after DeepSeek's launch, but the AI hype is still strong and growing.
• 😀 The overarching takeaway is that AI doesn't need to consume immense amounts of the world's energy to make significant progress, and there’s a growing focus on making AI more accessible.

Q & A

What is DeepSeek, and how does it compare to OpenAI's models?

DeepSeek is a new AI startup that created an open weights model called R1. This model allegedly outperforms OpenAI's best models in many metrics, while costing only $6 million and utilizing GPUs with half the memory bandwidth of OpenAI's models.

What makes DeepSeek's R1 model unique compared to OpenAI's models?

The DeepSeek R1 model is unique because it can distill other models to run more efficiently on slower hardware, such as a Raspberry Pi. This lowers the barriers to accessing powerful AI models, challenging OpenAI's resource-intensive approach.

Can a Raspberry Pi run DeepSeek's R1 model?

Yes, technically, a Raspberry Pi can run a smaller version of the DeepSeek model. However, the full R1 671b model, which outperforms OpenAI's ChatGPT, still requires significant GPU resources and cannot run effectively on a Raspberry Pi alone.

What hardware do you need to run the DeepSeek R1 model effectively?

To run the full DeepSeek R1 model effectively, you'll need high-performance GPUs, like the Nvidia 3090. However, smaller models can run on lower-end setups with enough RAM, and external GPUs can greatly improve performance.

How does adding an external graphics card improve DeepSeek's performance on a Raspberry Pi?

Adding an external graphics card significantly boosts the performance of DeepSeek on a Raspberry Pi, enabling faster processing. This is because GPUs with VRAM are much faster than CPUs and system memory, achieving up to 50 tokens per second in some cases.

What is the performance difference between running DeepSeek on a CPU versus a GPU?

Running DeepSeek on a CPU is much slower compared to a GPU. With an external AMD W7700 graphics card, performance can increase by 10 times, achieving between 20 and 50 tokens per second, depending on the task.

How can running AI models on GPUs help reduce resource consumption?

Running AI models on GPUs is more efficient than on CPUs, reducing the need for massive compute resources and excessive energy consumption. This contrasts with the current AI industry trend of relying on large-scale GPU farms.

What role do open-source drivers play in enabling AI on different hardware?

Open-source drivers for GPUs, such as the new Intel drivers, are enabling better AI performance on various hardware platforms, including Raspberry Pis and other ARM-based systems. This makes it easier for individuals to run AI models without relying on specialized, expensive hardware.

How does the rise of custom ARM and RISC-V PCs impact the AI landscape?

The rise of custom ARM and RISC-V PCs is changing the AI landscape by offering affordable and efficient alternatives to traditional x86-based systems. These platforms, equipped with full-size PCIe slots, make running AI models more accessible to a wider range of users.

What are the broader implications of DeepSeek's success for the AI industry?

DeepSeek's success challenges the dominance of companies like OpenAI by demonstrating that it is possible to create highly efficient AI models with significantly fewer resources. This could reduce the industry's reliance on massive data centers and reshape the way AI is developed and deployed.