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20VC: AI Chip Wars: How Cerebras Plans to Topple NVIDIA's Dominance | Why We Have Not Reached Scaling Laws in AI | What Happens to the Cost of Inference | How We Underestimate China and Shouldn't Sell To Them with Andrew Feldman
Mar 24, 2025
Andrew Feldman, Co-founder and CEO of Cerebras, shares deep insights into the AI chip landscape. He discusses how NVIDIA's strengths have become liabilities and why claims of reaching scaling laws in AI are misleading. Delving into the cost of inference, Feldman highlights the inefficiencies of algorithms and the necessity for a shift in AI architecture. He believes we underestimate China's tech advancements and critiques current U.S. policies in the realm of hardware export controls. Expect a thought-provoking analysis of AI's future and market dynamics!
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Quick takeaways
- Current AI algorithms are significantly underutilizing GPU capabilities, revealing tremendous opportunities for innovation in chip architecture and efficiency.
- Cerebras's wafer-scale chip technology optimizes memory usage, allowing for faster AI inference and training, directly challenging NVIDIA's dominance in the market.
Deep dives
Inefficiencies in Current AI Inference Systems
Current AI algorithms are significantly underutilizing GPU capabilities, with utilization rates as low as 5% to 7% during inference tasks. This inefficiency signals a major opportunity for improvement in chip architecture, especially concerning memory requirements for AI workloads. The prevailing architecture, notably the use of off-chip memory in GPUs, presents considerable limitations for efficient inference, as large amounts of data must constantly be moved. As processors continue to face these challenges, it is critical to innovate and develop alternatives that can better cater to the demands of AI.
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