Physical AI memory demand will be much larger than people expect

$MU has said humanoid robots carry 10x the memory content of an average L2+ vehicle
The average car today has around 16GB of DRAM, while L4 autonomous vehicles can require over 300GB
Humanoid robots are expected to use compute platforms comparable to high-end autonomous vehicles
Given that, if physical AI scaled to 100M humanoid robots, or compute-equivalent robots, the total DRAM need would be:
- 100M x 300GB = 30EB
That is equivalent to around 75% of 2026 global DRAM capacity
We can already see proof of this with NVIDIA’s Jetson Thor. Directed toward physical AI use cases, it comes with 128GB of LPDDR5X and 273GB/s of memory bandwidth, double the memory capacity of Jetson AGX Orin
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The reason for these memory requirements is that robots are not running a single small model
They need to process multiple cameras, sensors, depth data, audio, tactile inputs, and proprioception while running perception, reasoning, and real-time control loops
Vision-language-action models such as NVIDIA’s GR00T N1 also add memory pressure, as they combine visual understanding, language reasoning, and motor-policy generation
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The opportunity could be even bigger
Physical AI requires world models, simulation, synthetic data generation, policy training, fleet learning, and continuous retraining
NVIDIA’s Cosmos platform is an example of this, using massive video datasets and world foundation models to train and evaluate physical AI systems
This does not just increase demand for DRAM, it also explodes demand for NAND