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Nvidia nears $6t market cap as AI chip rally powers on
Nvidia shares rose as much as 4.7% to US$236.47 on May 14 as investors kept buying AI chip stocks.
The rally extended its seven-day gain to about 20% and pushed its market cap near US$6 trillion.
The Philadelphia Semiconductor Index has climbed nearly 70% since end-March as Intel and other chip stocks also rose.
Nvidia and Micron accounted for more than 30% of the S&P 500’s gain this year, adding to concerns that AI enthusiasm may be inflating a bubble.
Nvidia also drew attention after CEO Jensen Huang joined President Donald Trump on a China trip.
🔗 Source: Bloomberg
🧠 Food for thought
Implications, context, and why it matters.
Cerebras’s wafer-sized bet against standard chip design
- Nvidia cuts many small chips from a semiconductor wafer. Cerebras Systems, an AI chip company, turns the full wafer into one giant chip 1.
- That design packs in more computing capacity and uses on-chip SRAM (static random-access memory). Graphics processing units, or GPUs, usually rely on DRAM (dynamic random-access memory) 1.
- Data moves across shorter paths on a single large processor. Cerebras says this can deliver AI responses up to 15 times faster than leading GPU-based systems 1.
- The tradeoff is cost and complexity. That makes the design a niche option for demanding workloads, not a broad replacement for GPUs 1.
AI spending is moving from training toward efficiency
- Attention is shifting away from the upfront cost of training AI models. The bigger issue now is the ongoing cost of running them for users 2.
- That opens room for Cerebras in inference, the step where an AI system answers queries. Cerebras says its chips handle inference faster while using less power than Nvidia GPUs 2.
- The same shift helps explain why Amazon and Google are building their own chips to take on part of the work now handled by Nvidia processors 2.
- They want lower-cost computing options that use power more efficiently as they build the systems needed to keep AI models running and hold down expenses at scale 2.
Recent Nvidia developments
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