deepseek = deepsink for other AI players?

Training top-tier AI models is incredibly expensive. OpenAI, Anthropic, and others spend over $100M on compute, running extensively at the data centers filled with high-end GPUs—similar to needing an entire power plant to operate a factory.

Enter DeepSeek's R1 model, a new approach that achieved comparable or superior results to GPT-4 and Claude with just ~$5M. How? By rethinking AI architecture from the ground up.

Key Innovations

1. Memory Optimization:

   • Uses 8-bit precision instead of the standard 32-bit, reducing memory usage by 75% while maintaining high accuracy.

2. Multi-Token Prediction (MTP):

   • Unlike traditional models that predict one token at a time, MTP enables multiple-token predictions simultaneously, making R1 twice as fast with 90% of the accuracy of larger models.

3. Mixture of Experts (MoE) Architecture:

   • Sparse Activation: Instead of engaging all model parameters, R1 activates only 37B out of 671B parameters per input, significantly improving efficiency.

   • Expert Specialization: Different expert layers focus on specific domains like finance, law, and medicine, enhancing performance when needed.

   • Dynamic Routing: A smart gating function determines which expert layers activate, ensuring optimal computation.

The Results

• Training cost: $100M+ → $5M

• GPUs needed: 100,000 → 2,000

• API costs: 95% lower

• Runs on gaming GPUs rather data center hardware

and it’s open-source: DeepSeek-R1 @ Github. The entire codebase is available for review and further innovation. This doesn’t mean it has all of the glory; it has its limitations like it has higher hallucination rates than OpenAI O1; see Vectara's Hallucination Leaderboard.

Implications & Disruption

DeepSeek demonstrates that AI doesn’t require massive compute resources—smarter design can achieve similar results more efficiently. This challenges Nvidia’s dominance in high-end GPUs. If powerful AI can run on standard gaming GPUs, the landscape of AI development could shift significantly. Perhaps, the chips are down .

With a team of fewer than 200, DeepSeek is making an impact, while large companies like Meta spend more on compensation alone than DeepSeek’s entire training budget.

Looking Ahead

Big Tech is already adjusting, but the shift has begun—just as PCs replaced mainframes and cloud computing changed software deployment, efficient AI could redefine how models are built and deployed. The focus is moving from sheer computational power to smart, cost-effective innovation.

AI is becoming more accessible, affordable and democratized, opening doors for the broader adoption and development across industries. The next phase of AI may be shaped not by who has the most compute but by who uses it most efficiently.